Comparative analysis of the gut microbiota composition of healthy people and people with diarrhoea-predominant irritable bowel syndrome

Data preprocessing

Uploading, merging, and editing source datasets

info_healthy <- read_xlsx ("data/raw/final_health_statistic.xlsx") #metadata of healthy subjects 
info_ibs <- read_xlsx ("data/raw/final_ibs_141_statistic.xlsx") ##metadata of IBS subjects

combined_info dataset

#Combining `info_healthy` and `info_ibs` into a single dataset `combined_info` with subsequent content editing
combined_info <- info_healthy %>% 
  bind_rows(info_ibs) %>%
  mutate (
    BMI_min = ifelse (is.na (BMI_min), round (Weight_min /(Height_max/100 * Height_max/100), 2), BMI_min),
    BMI_max = ifelse (is.na (BMI_max), round (Weight_max /(Height_min/100 * Height_min/100), 2), BMI_max)
    ) %>% 
  unite("BMI_range", BMI_min, BMI_max, sep = "-", na.rm = TRUE) %>%
  unite ("Age_range", Age_min, Age_max, sep = "-", na.rm = TRUE) %>%
  mutate(
    Age_range = case_when(
      Age_range == "18-40" | Age_range == "23-28" | Age_range == "16-42" | Age_range == "21-43" ~ "16-43",
      Age <= 43 ~ "16-43",
      Age > 43 ~ "> 43",
      TRUE ~ NA_character_), #group 28-54 has been deleted
    research_ID = sub ("research_", "", research_ID),
    research_ID = case_when(
      research_ID == 0 ~ 1,
      research_ID == 1 ~ 2,
      research_ID == 2 ~ 3,
      research_ID == 3 ~ 4,
      research_ID == 4 ~ 5, 
      research_ID == 6 ~ 6, 
      research_ID == 7 ~ 7), 
    patient_ID = row_number(),
    Sex = ifelse (Sex == "mixed", NA, Sex),
    Smoking = sub ("never", "Never",  Smoking),
    Smoking = case_when(
      Smoking == "No" ~ "No",
      Smoking == "Never" ~ "No",
      Smoking == "Rarely (a few times/month)" ~ "Yes", #5 people
      Smoking == "Occasionally (1-2 times/week)" ~ "Yes",  #3 people
      Smoking == "Regularly (3-5 times/week)" ~ "Yes",  #1 people
      Smoking == "Daily" ~ "Yes"), #7 чел.
    Alcohol = sub ("rarely", "Rarely", Alcohol),
    Alcohol = ifelse(Alcohol == "Regularly (3-5 times/week)"|
                       Alcohol == "Daily", #3 чел.
                     "Regularly (3-7 times/week)", Alcohol),
    Antibiotics_usage = case_when(
      Antibiotics_usage == "Month" | Antibiotics_usage == ~ "3 months" |
        Antibiotics_usage == "6 months" ~ "1-6 months",
        #2 IBS people - Month, 0 healthy people - 3 months, 0 healthy people - 6 months
      Antibiotics_usage == "Year" | Antibiotics_usage == "Not use"  ~ 
        "12 months/Not use"), # 0 healthy people - 6-12 months, zero IBS people - Not use
    Hygiene = case_when(
      Hygiene == "Occasionally (1-2 times/week) cosmetics" ~ "Occasionally cosmetics (1-2 times/week)",
      Hygiene == "Rarely (a few times/month) cosmetics" ~ "Rarely cosmetics (a few times/month)",
      TRUE ~ Hygiene),
    Hygiene = case_when(
      Hygiene == "Daily cosmetics"|Hygiene == "Regularly cosmetics (3-5 times/week)" ~ "Regularly (3-7 times/week)", #0 чел. больных  для 3-5 times/week
      Hygiene == "Occasionally cosmetics (1-2 times/week)" | Hygiene == "Rarely cosmetics (a few times/month)" ~ "Occasionally (a few-8 times/month)",
      #только 2 чел. больных для 1-2 times/week
      Hygiene == "Never cosmetics" ~ "Never"),
    Physical_activity = sub ("regularly", "Regularly",  Smoking),
    BMI = ifelse (is.na(Weight_kg), BMI, Weight_kg/ (Height_cm/100 * Height_cm/100)),
    BMI_range = ifelse(BMI_range == "", NA, BMI_range),
    BMI_category = case_when(
      BMI_range == "18-25" ~ "normal/overweight",
      BMI_range == "19.21-29.29" ~ "normal/overweight",
      BMI_range == "20.6-29.6" ~ "normal/overweight",
      BMI_range == "21.74-28.38" ~ "normal/overweight",
      BMI_range == "18.5-30.8" ~ "normal/overweight", #a little over 30
      BMI < 18.5 ~ "underweight",
      BMI >= 18.5 & BMI < 30 ~ "normal/overweight",
      BMI >= 30 ~ "obese")
    ) %>% 
  rename ("Cosmetics" = Hygiene ) %>% 
  
  mutate_if (is.character, as.factor) %>% 
  
  select(-c(
    Instrument, # unique (combined_info$Instrument) = "Illumina MiSeq" 
    Isolation_source, # unique (combined_info$Isolation_source) = "faeces" 
    Assay_type, # unique (combined_info$Assay_type) = "AMPLICON"
    Target_gene, # unique (combined_info$Target_gene) = "16S"
    Main_Disease, # unique (combined_info$Main_Disease) = NA (for healthy), 141 (for IBS)
    Drugs, # unique (combined_info$Drugs) = NA
    Social_status, # unique (combined_info$Social_status) =  NA, urban 
    Weight_kg, Height_cm, Weight_min, Weight_max, Height_min, Height_max, BMI_range, #have been used to create the variable "BMI_category" and to reduce the amount of NA in "BMI"
    BMI, # NA for all healthy people
    Birth_Year, # no additional information
    Pets_type # unique (combined_info$Pets_type = cat, NA
  ))

rm (info_healthy, info_ibs)

#install.packages("summarytools")
library(summarytools)

saved_x11_option <- st_options("use.x11")
st_options(use.x11 = FALSE)

combined_info %>% 
  select(- patient_ID) %>% 
  dfSummary() %>% 
  print (method = "render")

Data Frame Summary

combined_info

Dimensions: 381 x 21
Duplicates: 244

No	Variable	Stats / Values	Freqs (% of Valid)	Valid	Missing
1	research_ID [numeric]	Mean (sd) : 4.2 (2) min ≤ med ≤ max: 1 ≤ 4 ≤ 7 IQR (CV) : 3 (0.5)	1 : 46 ( 12.1% ) 2 : 36 ( 9.4% ) 3 : 70 ( 18.4% ) 4 : 87 ( 22.8% ) 5 : 25 ( 6.6% ) 6 : 22 ( 5.8% ) 7 : 95 ( 24.9% )	381 (100.0%)	0 (0.0%)
2	Seq_region [factor]	1. V3-V4 2. V4 3. V5-V6	106 ( 27.8% ) 229 ( 60.1% ) 46 ( 12.1% )	381 (100.0%)	0 (0.0%)
3	Seq_date [numeric]	Mean (sd) : 2018.8 (2.9) min ≤ med ≤ max: 2015 ≤ 2018 ≤ 2023 IQR (CV) : 4 (0)	2015 : 95 ( 24.9% ) 2017 : 25 ( 6.6% ) 2018 : 96 ( 25.2% ) 2020 : 39 ( 10.2% ) 2021 : 32 ( 8.4% ) 2022 : 23 ( 6.0% ) 2023 : 71 ( 18.6% )	381 (100.0%)	0 (0.0%)
4	Health_state [factor]	1. Disease 2. Health	170 ( 44.6% ) 211 ( 55.4% )	381 (100.0%)	0 (0.0%)
5	Age [numeric]	Mean (sd) : 38.7 (13.8) min ≤ med ≤ max: 19 ≤ 34 ≤ 76 IQR (CV) : 19.8 (0.4)	45 distinct values	134 (35.2%)	247 (64.8%)
6	Age_range [factor]	1. > 43 2. 16-43	44 ( 15.4% ) 242 ( 84.6% )	286 (75.1%)	95 (24.9%)
7	Sex [factor]	1. female 2. male	79 ( 41.4% ) 112 ( 58.6% )	191 (50.1%)	190 (49.9%)
8	Country [factor]	1. Australia 2. Austria 3. Belgium 4. Germany 5. Greece 6. Hungary 7. Ireland 8. Israel 9. Italy 10. Norway [ 4 others ]	6 ( 1.6% ) 28 ( 7.3% ) 46 ( 12.1% ) 45 ( 11.8% ) 1 ( 0.3% ) 2 ( 0.5% ) 1 ( 0.3% ) 23 ( 6.0% ) 37 ( 9.7% ) 1 ( 0.3% ) 191 ( 50.1% )	381 (100.0%)	0 (0.0%)
9	Race [factor]	1. African American 2. Asian or Pacific Islander 3. Caucasian 4. Hispanic 5. Other	1 ( 0.8% ) 1 ( 0.8% ) 116 ( 89.2% ) 1 ( 0.8% ) 11 ( 8.5% )	130 (34.1%)	251 (65.9%)
10	Smoking [factor]	1. No 2. Yes	187 ( 92.1% ) 16 ( 7.9% )	203 (53.3%)	178 (46.7%)
11	Alcohol [factor]	1. Never 2. Occasionally (1-2 times/w 3. Rarely (a few times/month 4. Regularly (3-7 times/week	12 ( 13.8% ) 24 ( 27.6% ) 36 ( 41.4% ) 15 ( 17.2% )	87 (22.8%)	294 (77.2%)
12	Antibiotics_usage [factor]	1. 1-6 months 2. 12 months/Not use	48 ( 27.1% ) 129 ( 72.9% )	177 (46.5%)	204 (53.5%)
13	Physical_activity [factor]	1. No 2. Yes	187 ( 92.1% ) 16 ( 7.9% )	203 (53.3%)	178 (46.7%)
14	Travel_period [factor]	1. 3 months 2. 6 months 3. Month 4. Year	18 ( 20.9% ) 10 ( 11.6% ) 13 ( 15.1% ) 45 ( 52.3% )	86 (22.6%)	295 (77.4%)
15	Education_level [factor]	1. Bachelor's level 2. Master's level 3. School Level	31 ( 36.9% ) 28 ( 33.3% ) 25 ( 29.8% )	84 (22.0%)	297 (78.0%)
16	Cosmetics [factor]	1. Never 2. Occasionally (a few-8 tim 3. Regularly (3-7 times/week	44 ( 51.2% ) 17 ( 19.8% ) 25 ( 29.1% )	86 (22.6%)	295 (77.4%)
17	Sleep_duration [factor]	1. > 8 hours 2. 4-6 hours 3. 6-7 hours 4. 7-8 hours	9 ( 10.3% ) 9 ( 10.3% ) 31 ( 35.6% ) 38 ( 43.7% )	87 (22.8%)	294 (77.2%)
18	Diet_type [factor]	1. 10.0 2. 5.0	13 ( 72.2% ) 5 ( 27.8% )	18 (4.7%)	363 (95.3%)
19	Diet_duration [factor]	1. 3.0 2. 6.0	8 ( 61.5% ) 5 ( 38.5% )	13 (3.4%)	368 (96.6%)
20	Additive_usage [factor]	1. 22.0 2. 23.0 3. 4.0	1 ( 9.1% ) 1 ( 9.1% ) 9 ( 81.8% )	11 (2.9%)	370 (97.1%)
21	BMI_category [factor]	1. normal/overweight 2. obese 3. underweight	287 ( 98.0% ) 5 ( 1.7% ) 1 ( 0.3% )	293 (76.9%)	88 (23.1%)

Generated by summarytools 1.0.1 (R version 4.3.2)
2024-02-09

combined_info %>% 
  select (research_ID, Country, Seq_date, Seq_region, Health_state) %>% 
  mutate(Country = if_else (research_ID == 4, "Australia, Austria, Germany, Greece, Hungary, Ireland, Israel, Italy, Norway, UK, USA", Country),
         Seq_date = ifelse (research_ID == 4, "2018-2023", Seq_date),
         Health_state = if_else (research_ID == 4, "Disease/Health", Health_state)) %>% 
  group_by(research_ID, Country, Seq_date, Seq_region, Health_state) %>% 
  summarise(n = n()) %>% 
  flextable() %>% theme_box() %>% 
  merge_v("Health_state") %>% 
  align(align = "center", part = "all") %>% 
  set_caption("General characteristics of the included studies")

General characteristics of the included studies

research_ID	Country	Seq_date	Seq_region	Health_state	n
1	Belgium	2018	V5-V6	Health	46
2	Italy	2018	V3-V4		36
3	Poland	2023	V3-V4		70
4	Australia, Austria, Germany, Greece, Hungary, Ireland, Israel, Italy, Norway, UK, USA	2018-2023	V4	Disease/Health	87
5	Austria	2017	V4	Disease	25
6	Israel	2022	V4		22
7	Spain	2015	V4		95

tbl_summary of combined_info

library(gtsummary)

combined_info %>% 
  select(! c(patient_ID,
              Diet_duration, Additive_usage, Diet_type #for all healthy subjects = NA
              )) %>% 
  tbl_summary(digits = list(all_continuous() ~ c(0, 0),
                            all_categorical() ~ c(0, 0)),
              by = Health_state) %>%
  add_p()

Characteristic	Disease, N = 1701	Health, N = 2111	p-value2
research_ID			<0.001
1	0 (0%)	46 (22%)
2	0 (0%)	36 (17%)
3	0 (0%)	70 (33%)
4	28 (16%)	59 (28%)
5	25 (15%)	0 (0%)
6	22 (13%)	0 (0%)
7	95 (56%)	0 (0%)
Seq_region			<0.001
V3-V4	0 (0%)	106 (50%)
V4	170 (100%)	59 (28%)
V5-V6	0 (0%)	46 (22%)
Seq_date			<0.001
2015	95 (56%)	0 (0%)
2017	25 (15%)	0 (0%)
2018	14 (8%)	82 (39%)
2020	3 (2%)	36 (17%)
2021	9 (5%)	23 (11%)
2022	23 (14%)	0 (0%)
2023	1 (1%)	70 (33%)
Age	42 (32, 50)	28 (26, 37)	<0.001
Unknown	95	152
Age_range			<0.001
> 43	34 (45%)	10 (5%)
16-43	41 (55%)	201 (95%)
Unknown	95	0
Sex			0.15
female	35 (48%)	44 (37%)
male	38 (52%)	74 (63%)
Unknown	97	93
Country
Australia	0 (0%)	6 (3%)
Austria	25 (15%)	3 (1%)
Belgium	0 (0%)	46 (22%)
Germany	0 (0%)	45 (21%)
Greece	0 (0%)	1 (0%)
Hungary	0 (0%)	2 (1%)
Ireland	1 (1%)	0 (0%)
Israel	22 (13%)	1 (0%)
Italy	0 (0%)	37 (18%)
Norway	1 (1%)	0 (0%)
Poland	0 (0%)	70 (33%)
Spain	95 (56%)	0 (0%)
UK	12 (7%)	0 (0%)
USA	14 (8%)	0 (0%)
Race			0.4
African American	0 (0%)	1 (1%)
Asian or Pacific Islander	0 (0%)	1 (1%)
Caucasian	27 (100%)	89 (86%)
Hispanic	0 (0%)	1 (1%)
Other	0 (0%)	11 (11%)
Unknown	143	108
Smoking	4 (14%)	12 (7%)	0.2
Unknown	142	36
Alcohol			0.002
Never	4 (14%)	8 (14%)
Occasionally (1-2 times/week)	4 (14%)	20 (34%)
Rarely (a few times/month)	9 (32%)	27 (46%)
Regularly (3-7 times/week)	11 (39%)	4 (7%)
Unknown	142	152
Antibiotics_usage			0.020
1-6 months	2 (8%)	46 (30%)
12 months/Not use	23 (92%)	106 (70%)
Unknown	145	59
Physical_activity	4 (14%)	12 (7%)	0.2
Unknown	142	36
Travel_period			0.066
3 months	3 (11%)	15 (26%)
6 months	1 (4%)	9 (16%)
Month	4 (14%)	9 (16%)
Year	20 (71%)	25 (43%)
Unknown	142	153
Education_level			<0.001
Bachelor's level	5 (18%)	26 (46%)
Master's level	18 (64%)	10 (18%)
School Level	5 (18%)	20 (36%)
Unknown	142	155
Cosmetics			0.6
Never	15 (56%)	29 (49%)
Occasionally (a few-8 times/month)	6 (22%)	11 (19%)
Regularly (3-7 times/week)	6 (22%)	19 (32%)
Unknown	143	152
Sleep_duration			0.2
> 8 hours	4 (14%)	5 (8%)
4-6 hours	4 (14%)	5 (8%)
6-7 hours	12 (43%)	19 (32%)
7-8 hours	8 (29%)	30 (51%)
Unknown	142	152
BMI_category			0.012
normal/overweight	135 (96%)	152 (100%)
obese	5 (4%)	0 (0%)
underweight	1 (1%)	0 (0%)
Unknown	29	59
1 n (%); Median (IQR)
2 Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test

###combined_bacteria and data_wide (not batched) datasets

bacteria_healthy <- read_csv("data/raw/final_bacteria_health.csv") #abundance table for healthy
bacteria_ibs <- read_csv("data/raw/final_bacteria_ibs_141.csv") #abundance table for healthy

combined_bacteria <- bacteria_healthy %>% 
  bind_rows (bacteria_ibs) %>% 
  mutate(patient_ID = row_number())

rm (bacteria_healthy, bacteria_ibs)

# Combining `combined_info` and `combined_bacteria` into `data_wide`
data_wide <- combined_info %>% left_join (combined_bacteria)

 

Estimation of the NA-proportion and zero values

data_wide %>% 
  select (where (function(x) sum (is.na(x))/ nrow(data_wide) * 100 > 0)) %>% 
  sapply (function(x) sum (is.na(x))/ nrow(data_wide) * 100) %>% round(1) %>% 
  as.data.frame() %>% 
  rename(NA_percentage = ".") %>% 
  mutate (
    "Number of people with known data" = round (nrow(data_wide) - NA_percentage/100 * nrow(data_wide)),
    NA_percentage = paste (NA_percentage, "%", sep = " ")
    ) %>% 
  arrange(desc (NA_percentage)) %>% 
  rownames_to_column() %>% 
  as_tibble() %>% flextable()

rowname	NA_percentage	Number of people with known data
Additive_usage	97.1 %	11
Diet_duration	96.6 %	13
Diet_type	95.3 %	18
Education_level	78 %	84
Travel_period	77.4 %	86
Cosmetics	77.4 %	86
Alcohol	77.2 %	87
Sleep_duration	77.2 %	87
Race	65.9 %	130
Age	64.8 %	134
Antibiotics_usage	53.5 %	177
Sex	49.9 %	191
Smoking	46.7 %	203
Physical_activity	46.7 %	203
Age_range	24.9 %	286
BMI_category	23.1 %	293

• Estimation of the NA-proportion and zero values in variables of data_wide

# Устанавливаем порог процента 
threshold_percent <- 95 
 
# Функция для вычисления процента записей, не равных NA и не равных 0, для каждой колонки 
calculate_percentage <- function(col) { 
  sum(!is.na(col) & col != 0) / length(col) * 100 
} 
 
# Применяем функцию к каждой колонке в датасете 
percentage_non_zero_non_na <- sapply(data_wide[, -1], calculate_percentage) 
 
# Создаем датафрейм с результатами 
result_df_sort <- data.frame( 
  column = names(percentage_non_zero_non_na), 
  percentage = round(100 - percentage_non_zero_non_na, 2) # percentage означает пропущенные или 0 значения
) %>% 
  arrange(desc(percentage))
 
# Отфильтровываем колонки, у которых процент записей менее threshold_percent% 
filtered_columns <- result_df_sort[result_df_sort$percentage < threshold_percent, ]

# Сохраним датасет в excel для дальнейшего анализа
write.xlsx(filtered_columns, 
           file = "data/originals/percentage_by_vars.xlsx")

# Перезапись data_wide с выбором колонок с процентом NA/0 менее threshold_percent 
data_wide <- data_wide %>% 
  select (row.names(filtered_columns), research_ID)

rm (calculate_percentage, result_df_sort)

• Estimation of the NA-proportion and zero values in cases of data_wide

# Устанавливаем порог процента 
threshold_percent <- 95
 
# Рассчитываем процент значений, не являющихся NA и не равных 0, для каждого пациента 
percentage_non_zero_non_na <- rowMeans(!is.na(data_wide) & data_wide != 0, na.rm = TRUE) * 100 
 
# Создаем датафрейм с результатами 
result_df_sort <- data.frame( 
  patient_id = data_wide$patient_ID, 
  percentage = round(100 - percentage_non_zero_non_na, 2) # percentage означает пропущенные или 0 значения
) %>% arrange(desc(percentage))

# Отфильтровываем пациентов, у которых процент значений менее threshold_percent% 
filtered_patients <- result_df_sort[result_df_sort$percentage < threshold_percent, ] 

# Сохраним датасет в excel для дальнейшего анализа 
write.xlsx(filtered_patients,
           file = "data/originals/percentage_by_patient.xlsx") 

# Перезапись data_wide с удалением строк с процентом NA/0 более threshold_percent 
data_wide <- data_wide %>% 
  slice (filtered_patients$patient_id) #при threshold_percent = 95%, изменения data_wide не происходит, так как нет пациентов с процентом NA/0 более 95%

rm (percentage_non_zero_non_na, result_df_sort)

# Deleting columns and rows with a NA/0-percentage more than threshold_percent from `data_wide`
data_wide <- data_wide %>% 
  select (patient_ID, any_of (colnames(combined_info)), everything()) %>% 
  arrange(patient_ID)

 

Search for “single study taxa”

• G-taxa

G_only_one <- data_wide %>% 
  select (research_ID, ends_with("_G")) %>%
  add_column(n = 1) %>% #for further counting the subjects number in each study
  group_by(research_ID) %>% 
  summarise_each (sum) %>% 
  select (research_ID, n, where (function(x) sum (x!=0) == 1)) %>%  #selecting "single study taxa"
  mutate (across(-c(1:2),
                 function(x) x/n))

G_only_one %>% as_tibble() %>% flextable()

research_ID	n	CM1G08_G	Cladosporium_G	Lentimonas_G	Micromonospora_G	Pseudosphingobacterium_G	Schizothrix LEGE 07164_G	Talaromyces_G	Rs-D38 termite group_G	Kabatiella_G	Anaerolineaceae UCG-001_G	Iamia_G	Thiohalocapsa_G	Ellin517_G
1	46	0.000000000	0.000000000	0.05001457	0.03314097	0.01653521	0.01087879	0.00000000	0.000000000	0.00000000	0.06983517	0.03046327	0.2743871	0.000000
2	36	0.000000000	0.008105222	0.00000000	0.00000000	0.00000000	0.00000000	0.01425858	0.000000000	0.02177045	0.00000000	0.00000000	0.0000000	0.000000
3	70	0.000000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000	0.004717417	0.00000000	0.00000000	0.00000000	0.0000000	0.000000
4	87	0.005504621	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.0000000	0.000000
5	25	0.000000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.0000000	0.000000
6	22	0.000000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.0000000	0.000000
7	95	0.000000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.0000000	1.594206

#Determining the percentage of people in whom these taxa have not been detected
G_taxon <- 'Ellin517_G'
res_ID <- 7

#data_wide %>% 
  #select (research_ID, G_taxon) %>%
  #filter (research_ID == res_ID) %>% 
  #summarise(across (-1,
              #function (x) sum (x==0)/nrow (.) * 100)) %>% 
  #rename ("Taxon_zero_percentage, %" = G_taxon) 

# CM1G08_G - 73,6% нулей
# Cladosporium_G - 94,0% нулей
# Lentimonas_G - 94,0% нулей
# Micromonospora_G - 93,2% нулей
# Pseudosphingobacterium_G - 93,2% нулей 
# Schizothrix LEGE 07164_G - 92,7% нулей 
# Talaromyces_G - 92,7% нулей 
# Rs-D38 termite group_G - 91,9% нулей 
# Kabatiella_G - 90,6% нулей 
# Anaerolineaceae UCG-001_G - 89,5% нулей 
# Iamia_G - 88,2% нулей 
# Thiohalocapsa_G - 87,9% нулей 
# Ellin517_G - 75,1% нулей 

• F-taxa

data_wide %>% 
  select (research_ID, ends_with("_F")) %>%
  add_column(n = 1) %>% # for further counting the subjects number in each study
  group_by(research_ID) %>% 
  summarise_each (sum) %>% 
  select (research_ID, n, where (function(x) sum (x!=0) == 1)) %>%  #selecting "single study taxa"
  mutate (across(-c(1:2), function(x) x/n)) %>% 
  as_tibble() %>% flextable()

research_ID	n	Didymellaceae_F	Cladosporiaceae_F	Trichocomaceae_F	type III_F	09D2Z48_F	Aureobasidiaceae_F	Iamiaceae_F
1	46	0.00000000	0.000000000	0.00000000	0.01131476	0.01196557	0.00000000	0.03046327
2	36	0.02128992	0.008105222	0.01425858	0.00000000	0.00000000	0.02177045	0.00000000
3	70	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000
4	87	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000
5	25	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000
6	22	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000
7	95	0.00000000	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	0.00000000

#Determining the percentage of people in whom these taxa have not been detected
#F_taxon <- 'Iamiaceae_F'
#res_ID <- 1

#data_wide %>% 
  #select (research_ID, F_taxon) %>%
  #filter (research_ID == res_ID) %>% 
  #summarise(across (-1,
              #function (x) sum (x==0)/nrow (.) * 100)) %>% 
  #rename ("Taxon_zero_percentage, %" = F_taxon) 

# Didymellaceae_F - 44,4% нулей
# Cladosporiaceae_F - 36,1% нулей
# Trichocomaceae_F - 22,2% нулей
# type III_F - 30,4% нулей
# 09D2Z48_F - 23,9% нулей
# Aureobasidiaceae_F - 0% нулей 
# Iamiaceae_F - 2,2% нулей

• O-taxa

data_wide %>% 
  select (research_ID, ends_with("_O")) %>%
  add_column(n = 1) %>% #for further counting the subjects number in each study
  group_by(research_ID) %>% 
  summarise_each (sum) %>% 
  select (research_ID, n, where (function(x) sum (x!=0) == 1)) %>%  #selecting "single study taxa"
  mutate (across(-c(1:2), function(x) x/n)) %>% 
  as_tibble() %>% flextable()

research_ID	n	Hypocreales_O	Pleosporales_O	Candidatus Abawacabacteria_O	Candidatus Peregrinibacteria_O	Capnodiales_O	Candidatus Terrybacteria_O	Eurotiales_O	Dothideales_O	eub62A3_O	LD1-PA32_O
1	46	0.00000000	0.00000000	0.008918278	0.007617552	0.00000000	0.007831183	0.00000000	0.00000000	0.0130492	0.05113126
2	36	0.06219381	0.03669163	0.000000000	0.000000000	0.01202125	0.000000000	0.01425858	0.02680276	0.0000000	0.00000000
3	70	0.00000000	0.00000000	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.0000000	0.00000000
4	87	0.00000000	0.00000000	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.0000000	0.00000000
5	25	0.00000000	0.00000000	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.0000000	0.00000000
6	22	0.00000000	0.00000000	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.0000000	0.00000000
7	95	0.00000000	0.00000000	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.0000000	0.00000000

#Determining the percentage of people in whom these taxa have not been detected
#O_taxon <- 'Candidatus Abawacabacteria_O'
#res_ID <- 1

#data_wide %>% 
  #select (research_ID, O_taxon) %>%
  #filter (research_ID == res_ID) %>% 
  #summarise(across (-1, function (x) sum (x==0)/nrow (.) * 100)) %>% 
  #rename ("Taxon_zero_percentage, %" = O_taxon) 

# Hypocreales_O - 41,7% нулей
# Pleosporales_O - 41,7% нулей
# Candidatus Abawacabacteria_O - 52,2% нулей
# Candidatus Peregrinibacteria_O - 52,2% нулей
# Capnodiales_O - 33,3% нулей
# Candidatus Terrybacteria_O - 39,1% нулей
# Eurotiales_O - 39,1% нулей
# Dothideales_O - 0% нулей
# eub62A3_O - 15,2% нулей
# LD1-PA32_O - 2,2% нулей

• C-taxa

data_wide %>% 
  select (research_ID, ends_with("_C")) %>%
  add_column(n = 1) %>% #for further counting the subjects number in each study
  group_by(research_ID) %>% 
  summarise_each (sum) %>% 
  select (research_ID, n, where (function(x) sum (x!=0) == 1)) %>%  #selecting "single study taxa"
  mutate (across(-c(1:2), function(x) x/n)) %>% 
  as_tibble() %>% flextable()

research_ID	n	WWE3_C	Eurotiomycetes_C	Dothideomycetes_C
1	46	0.00000000	0.00000000	0.0000000
2	36	0.00000000	0.02293748	0.1015485
3	70	0.00000000	0.00000000	0.0000000
4	87	0.01350327	0.00000000	0.0000000
5	25	0.00000000	0.00000000	0.0000000
6	22	0.00000000	0.00000000	0.0000000
7	95	0.00000000	0.00000000	0.0000000

#Determining the percentage of people in whom these taxa have not been detected
#С_taxon <- 'Dothideomycetes_C'
#res_ID <- 2

#data_wide %>% 
  #select (research_ID, С_taxon) %>%
  #filter (research_ID == res_ID) %>% 
  #summarise(across (-1, function (x) sum (x==0)/nrow (.) * 100)) %>% 
  #rename ("Taxon_zero_percentage, %" = С_taxon) 

# WWE3_C - 74,4% нулей
# Eurotiomycetes_C - 19,4% нулей 
# Dothideomycetes_C - 0% нулей 

• P-taxa

“Single study P-taxa” have not been detected

   

data_wide_not_batched dataset

data_wide <- data_wide %>% 
  select (!colnames (G_only_one) [- c (1,2)]) #deleting of single study G-taxa (in this single study the taxa were present in no more than 25% of subjects)

data_wide_not_batched <- data_wide  

write_rds(data_wide, 
          file = "data/originals/data_wide_not_batched.rds")

Clustering before batch effect correction

G-taxa clustering

AGNES (euclidean distance matrix)

clusters_number <- 7 #number of clusters

library(cluster)
library(factoextra)

G_scaled <- data_wide %>% 
  mutate(patient_ID = paste0("patient_", patient_ID)) %>% 
  column_to_rownames("patient_ID") %>% 
  select (ends_with("_G")) %>% 
  scale () 

agnes <- G_scaled %>% 
  dist (method = "euclidean") %>% 
  as.matrix() %>%  
  agnes( #Agglomerative clustering
    diss = TRUE, #dissimilarity matrix
    method = "ward")

fviz_dend(agnes,
          k = clusters_number,
          rect = TRUE,
          k_colors = "jco",
          show_labels = TRUE,
          label_cols = ifelse (data_wide[agnes$order,]$Health_state == "Health", "green", "red"),
          #labels colors = Health_state
          cex = 0.2,
          main = "Cluster dengrogram based on G-taxa abundance before batch effect correction",
          ylab = "")

• Comparison of the clusters

data_wide %>% 
  add_column("Cluster" = cutree(agnes, k = clusters_number)) %>% 
  select(any_of(colnames (combined_info)), Cluster, - patient_ID) %>% 
  tbl_summary(digits = list(all_continuous() ~ c(0, 0),
                            all_categorical() ~ c(0, 0)),
              by = Cluster) %>% 
  add_p()

Characteristic	1, N = 461	2, N = 311	3, N = 1891	4, N = 231	5, N = 651	6, N = 21	7, N = 251	p-value2
research_ID
1	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2	0 (0%)	31 (100%)	4 (2%)	1 (4%)	0 (0%)	0 (0%)	0 (0%)
3	0 (0%)	0 (0%)	2 (1%)	3 (13%)	65 (100%)	0 (0%)	0 (0%)
4	0 (0%)	0 (0%)	66 (35%)	19 (83%)	0 (0%)	2 (100%)	0 (0%)
5	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	25 (100%)
6	0 (0%)	0 (0%)	22 (12%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
7	0 (0%)	0 (0%)	95 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Seq_region
V3-V4	0 (0%)	31 (100%)	6 (3%)	4 (17%)	65 (100%)	0 (0%)	0 (0%)
V4	0 (0%)	0 (0%)	183 (97%)	19 (83%)	0 (0%)	2 (100%)	25 (100%)
V5-V6	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Seq_date
2015	0 (0%)	0 (0%)	95 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2017	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	25 (100%)
2018	46 (100%)	31 (100%)	11 (6%)	7 (30%)	0 (0%)	1 (50%)	0 (0%)
2020	0 (0%)	0 (0%)	36 (19%)	2 (9%)	0 (0%)	1 (50%)	0 (0%)
2021	0 (0%)	0 (0%)	21 (11%)	11 (48%)	0 (0%)	0 (0%)	0 (0%)
2022	0 (0%)	0 (0%)	23 (12%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2023	0 (0%)	0 (0%)	3 (2%)	3 (13%)	65 (100%)	0 (0%)	0 (0%)
Health_state
Disease	0 (0%)	0 (0%)	134 (71%)	10 (43%)	0 (0%)	1 (50%)	25 (100%)
Health	46 (100%)	31 (100%)	55 (29%)	13 (57%)	65 (100%)	1 (50%)	0 (0%)
Age	NA (NA, NA)	NA (NA, NA)	32 (27, 42)	49 (43, 52)	NA (NA, NA)	32 (32, 33)	39 (28, 49)	0.002
Unknown	46	31	101	4	65	0	0
Age_range
> 43	0 (0%)	0 (0%)	21 (22%)	13 (57%)	0 (0%)	0 (0%)	10 (40%)
16-43	46 (100%)	31 (100%)	73 (78%)	10 (43%)	65 (100%)	2 (100%)	15 (60%)
Unknown	0	0	95	0	0	0	0
Sex								<0.001
female	0 (NA%)	0 (0%)	20 (42%)	5 (24%)	38 (58%)	0 (0%)	16 (64%)
male	0 (NA%)	31 (100%)	28 (58%)	16 (76%)	27 (42%)	1 (100%)	9 (36%)
Unknown	46	0	141	2	0	1	0
Country
Australia	0 (0%)	0 (0%)	2 (1%)	4 (17%)	0 (0%)	0 (0%)	0 (0%)
Austria	0 (0%)	0 (0%)	0 (0%)	2 (9%)	0 (0%)	1 (50%)	25 (100%)
Belgium	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Germany	0 (0%)	0 (0%)	45 (24%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Greece	0 (0%)	0 (0%)	1 (1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Hungary	0 (0%)	0 (0%)	0 (0%)	2 (9%)	0 (0%)	0 (0%)	0 (0%)
Ireland	0 (0%)	0 (0%)	0 (0%)	1 (4%)	0 (0%)	0 (0%)	0 (0%)
Israel	0 (0%)	0 (0%)	23 (12%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Italy	0 (0%)	31 (100%)	4 (2%)	2 (9%)	0 (0%)	0 (0%)	0 (0%)
Norway	0 (0%)	0 (0%)	1 (1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Poland	0 (0%)	0 (0%)	2 (1%)	3 (13%)	65 (100%)	0 (0%)	0 (0%)
Spain	0 (0%)	0 (0%)	95 (50%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
UK	0 (0%)	0 (0%)	8 (4%)	3 (13%)	0 (0%)	1 (50%)	0 (0%)
USA	0 (0%)	0 (0%)	8 (4%)	6 (26%)	0 (0%)	0 (0%)	0 (0%)
Race								0.016
African American	0 (0%)	0 (NA%)	1 (2%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)
Asian or Pacific Islander	0 (0%)	0 (NA%)	1 (2%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)
Caucasian	46 (100%)	0 (NA%)	49 (78%)	19 (100%)	0 (NA%)	2 (100%)	0 (NA%)
Hispanic	0 (0%)	0 (NA%)	1 (2%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)
Other	0 (0%)	0 (NA%)	11 (17%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)
Unknown	0	31	126	4	65	0	25
Smoking	0 (0%)	0 (NA%)	14 (21%)	2 (9%)	0 (0%)	0 (0%)	0 (NA%)	<0.001
Unknown	0	31	121	1	0	0	25
Alcohol								0.8
Never	0 (NA%)	0 (NA%)	8 (12%)	4 (21%)	0 (NA%)	0 (0%)	0 (NA%)
Occasionally (1-2 times/week)	0 (NA%)	0 (NA%)	20 (30%)	4 (21%)	0 (NA%)	0 (0%)	0 (NA%)
Rarely (a few times/month)	0 (NA%)	0 (NA%)	26 (39%)	8 (42%)	0 (NA%)	2 (100%)	0 (NA%)
Regularly (3-7 times/week)	0 (NA%)	0 (NA%)	12 (18%)	3 (16%)	0 (NA%)	0 (0%)	0 (NA%)
Unknown	46	31	123	4	65	0	25
Antibiotics_usage								<0.001
1-6 months	46 (100%)	0 (0%)	1 (5%)	1 (8%)	0 (0%)	0 (0%)	0 (NA%)
12 months/Not use	0 (0%)	31 (100%)	21 (95%)	11 (92%)	65 (100%)	1 (100%)	0 (NA%)
Unknown	0	0	167	11	0	1	25
Physical_activity	0 (0%)	0 (NA%)	14 (21%)	2 (9%)	0 (0%)	0 (0%)	0 (NA%)	<0.001
Unknown	0	31	121	1	0	0	25
Travel_period								0.019
3 months	0 (NA%)	0 (NA%)	12 (18%)	6 (33%)	0 (NA%)	0 (0%)	0 (NA%)
6 months	0 (NA%)	0 (NA%)	10 (15%)	0 (0%)	0 (NA%)	0 (0%)	0 (NA%)
Month	0 (NA%)	0 (NA%)	7 (11%)	4 (22%)	0 (NA%)	2 (100%)	0 (NA%)
Year	0 (NA%)	0 (NA%)	37 (56%)	8 (44%)	0 (NA%)	0 (0%)	0 (NA%)
Unknown	46	31	123	5	65	0	25
Education_level								0.006
Bachelor's level	0 (NA%)	0 (NA%)	26 (41%)	5 (28%)	0 (NA%)	0 (0%)	0 (NA%)
Master's level	0 (NA%)	0 (NA%)	15 (23%)	11 (61%)	0 (NA%)	2 (100%)	0 (NA%)
School Level	0 (NA%)	0 (NA%)	23 (36%)	2 (11%)	0 (NA%)	0 (0%)	0 (NA%)
Unknown	46	31	125	5	65	0	25
Cosmetics								0.2
Never	0 (NA%)	0 (NA%)	30 (46%)	13 (68%)	0 (NA%)	1 (50%)	0 (NA%)
Occasionally (a few-8 times/month)	0 (NA%)	0 (NA%)	16 (25%)	1 (5%)	0 (NA%)	0 (0%)	0 (NA%)
Regularly (3-7 times/week)	0 (NA%)	0 (NA%)	19 (29%)	5 (26%)	0 (NA%)	1 (50%)	0 (NA%)
Unknown	46	31	124	4	65	0	25
Sleep_duration								0.2
> 8 hours	0 (NA%)	0 (NA%)	6 (9%)	3 (16%)	0 (NA%)	0 (0%)	0 (NA%)
4-6 hours	0 (NA%)	0 (NA%)	5 (8%)	4 (21%)	0 (NA%)	0 (0%)	0 (NA%)
6-7 hours	0 (NA%)	0 (NA%)	22 (33%)	8 (42%)	0 (NA%)	1 (50%)	0 (NA%)
7-8 hours	0 (NA%)	0 (NA%)	33 (50%)	4 (21%)	0 (NA%)	1 (50%)	0 (NA%)
Unknown	46	31	123	4	65	0	25
BMI_category								0.10
normal/overweight	46 (100%)	31 (100%)	133 (97%)	11 (85%)	65 (100%)	1 (100%)	0 (NA%)
obese	0 (0%)	0 (0%)	3 (2%)	2 (15%)	0 (0%)	0 (0%)	0 (NA%)
underweight	0 (0%)	0 (0%)	1 (1%)	0 (0%)	0 (0%)	0 (0%)	0 (NA%)
Unknown	0	0	52	10	0	1	25
1 n (%); Median (IQR)
2 Kruskal-Wallis rank sum test; Fisher’s exact test

AGNES (distance matrix for Bray-Curtis dissimilarity)

agnes2 <- data_wide %>% 
  column_to_rownames("patient_ID") %>% 
  select (ends_with("_G")) %>% 
  vegdist(method = "bray") %>% #distance matrix for Bray-Curtis dissimilarity:
  #the Bray–Curtis dissimilarity is bounded between 0 and 1, where 0 means the two sites have the same composition , and 1 means the two sites do not share any species
  as.matrix() %>% 
  agnes( #Agglomerative clustering 
    diss = TRUE, #dissimilarity matrix
    method = "ward")

fviz_dend(agnes2, 
          cex = 0.2, k = clusters_number,
          rect = TRUE,
          k_colors = "jco",
          color_labels_by_k = FALSE,
          label_cols = ifelse (data_wide[agnes2$order,]$Health_state == "Health", "green", "red"),
          #labels colors = Health_state
          main = "Cluster dengrogram based on G-taxa abundance before batch effect correction",
          ylab = "")

• Comparison of the clusters

fisher.test.simulate.p.values <- function(data, variable, by, ...) {
  result <- list()
  test_results <- stats::fisher.test(data[[variable]], data[[by]], simulate.p.value = TRUE)
  result$p <- test_results$p.value
  result$test <- test_results$method
  result
}

data_wide %>% 
  add_column("Cluster" = cutree(agnes2, k = clusters_number)) %>%
  select(any_of(colnames (combined_info)), Cluster, - patient_ID) %>% 
  tbl_summary(digits = list(all_continuous() ~ c(0, 0),
                            all_categorical() ~ c(0, 0)),
              by = Cluster) %>% 
  add_p(test = list(all_categorical() ~ "fisher.test.simulate.p.values"))

Characteristic	1, N = 461	2, N = 701	3, N = 1211	4, N = 411	5, N = 461	6, N = 211	7, N = 361	p-value2
research_ID								<0.001
1	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
2	0 (0%)	19 (27%)	5 (4%)	8 (20%)	3 (7%)	1 (5%)	0 (0%)
3	0 (0%)	25 (36%)	26 (21%)	8 (20%)	9 (20%)	0 (0%)	2 (6%)
4	0 (0%)	6 (9%)	36 (30%)	0 (0%)	9 (20%)	20 (95%)	16 (44%)
5	0 (0%)	2 (3%)	10 (8%)	0 (0%)	0 (0%)	0 (0%)	13 (36%)
6	0 (0%)	4 (6%)	9 (7%)	3 (7%)	4 (9%)	0 (0%)	2 (6%)
7	0 (0%)	14 (20%)	35 (29%)	22 (54%)	21 (46%)	0 (0%)	3 (8%)
Seq_region								<0.001
V3-V4	0 (0%)	44 (63%)	31 (26%)	16 (39%)	12 (26%)	1 (5%)	2 (6%)
V4	0 (0%)	26 (37%)	90 (74%)	25 (61%)	34 (74%)	20 (95%)	34 (94%)
V5-V6	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Seq_date								<0.001
2015	0 (0%)	14 (20%)	35 (29%)	22 (54%)	21 (46%)	0 (0%)	3 (8%)
2017	0 (0%)	2 (3%)	10 (8%)	0 (0%)	0 (0%)	0 (0%)	13 (36%)
2018	46 (100%)	21 (30%)	8 (7%)	8 (20%)	5 (11%)	7 (33%)	1 (3%)
2020	0 (0%)	2 (3%)	20 (17%)	0 (0%)	5 (11%)	2 (10%)	10 (28%)
2021	0 (0%)	2 (3%)	11 (9%)	0 (0%)	2 (4%)	12 (57%)	5 (14%)
2022	0 (0%)	4 (6%)	10 (8%)	3 (7%)	4 (9%)	0 (0%)	2 (6%)
2023	0 (0%)	25 (36%)	27 (22%)	8 (20%)	9 (20%)	0 (0%)	2 (6%)
Health_state								<0.001
Disease	0 (0%)	23 (33%)	63 (52%)	25 (61%)	29 (63%)	10 (48%)	20 (56%)
Health	46 (100%)	47 (67%)	58 (48%)	16 (39%)	17 (37%)	11 (52%)	16 (44%)
Age	NA (NA, NA)	36 (29, 47)	33 (27, 48)	35 (30, 41)	32 (29, 38)	48 (42, 56)	31 (26, 41)	0.010
Unknown	46	58	66	38	33	1	5
Age_range								<0.001
> 43	0 (0%)	5 (9%)	15 (17%)	1 (5%)	3 (12%)	13 (62%)	7 (21%)
16-43	46 (100%)	51 (91%)	71 (83%)	18 (95%)	22 (88%)	8 (38%)	26 (79%)
Unknown	0	14	35	22	21	0	3
Sex								0.027
female	0 (NA%)	18 (34%)	36 (59%)	4 (21%)	7 (37%)	6 (32%)	8 (40%)
male	0 (NA%)	35 (66%)	25 (41%)	15 (79%)	12 (63%)	13 (68%)	12 (60%)
Unknown	46	17	60	22	27	2	16
Country								<0.001
Australia	0 (0%)	0 (0%)	1 (1%)	0 (0%)	0 (0%)	4 (19%)	1 (3%)
Austria	0 (0%)	2 (3%)	10 (8%)	0 (0%)	0 (0%)	2 (10%)	14 (39%)
Belgium	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Germany	0 (0%)	3 (4%)	25 (21%)	0 (0%)	5 (11%)	0 (0%)	12 (33%)
Greece	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (5%)	0 (0%)
Hungary	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (10%)	0 (0%)
Ireland	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (5%)	0 (0%)
Israel	0 (0%)	4 (6%)	10 (8%)	3 (7%)	4 (9%)	0 (0%)	2 (6%)
Italy	0 (0%)	19 (27%)	5 (4%)	8 (20%)	3 (7%)	2 (10%)	0 (0%)
Norway	0 (0%)	0 (0%)	1 (1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Poland	0 (0%)	25 (36%)	26 (21%)	8 (20%)	9 (20%)	0 (0%)	2 (6%)
Spain	0 (0%)	14 (20%)	35 (29%)	22 (54%)	21 (46%)	0 (0%)	3 (8%)
UK	0 (0%)	2 (3%)	1 (1%)	0 (0%)	4 (9%)	4 (19%)	1 (3%)
USA	0 (0%)	1 (1%)	7 (6%)	0 (0%)	0 (0%)	5 (24%)	1 (3%)
Race								0.001
African American	0 (0%)	0 (0%)	1 (3%)	0 (NA%)	0 (0%)	0 (0%)	0 (0%)
Asian or Pacific Islander	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	1 (11%)	0 (0%)	0 (0%)
Caucasian	46 (100%)	6 (100%)	28 (80%)	0 (NA%)	7 (78%)	20 (100%)	9 (64%)
Hispanic	0 (0%)	0 (0%)	0 (0%)	0 (NA%)	0 (0%)	0 (0%)	1 (7%)
Other	0 (0%)	0 (0%)	6 (17%)	0 (NA%)	1 (11%)	0 (0%)	4 (29%)
Unknown	0	64	86	41	37	1	22
Smoking	0 (0%)	2 (6%)	7 (11%)	0 (0%)	1 (6%)	3 (15%)	3 (17%)	0.090
Unknown	0	39	59	33	28	1	18
Alcohol								0.7
Never	0 (NA%)	0 (0%)	5 (14%)	0 (NA%)	1 (11%)	5 (25%)	1 (6%)
Occasionally (1-2 times/week)	0 (NA%)	1 (17%)	12 (33%)	0 (NA%)	1 (11%)	4 (20%)	6 (38%)
Rarely (a few times/month)	0 (NA%)	4 (67%)	12 (33%)	0 (NA%)	4 (44%)	8 (40%)	8 (50%)
Regularly (3-7 times/week)	0 (NA%)	1 (17%)	7 (19%)	0 (NA%)	3 (33%)	3 (15%)	1 (6%)
Unknown	46	64	85	41	37	1	20
Antibiotics_usage								<0.001
1-6 months	46 (100%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (11%)	1 (25%)
12 months/Not use	0 (0%)	47 (100%)	39 (100%)	16 (100%)	16 (100%)	8 (89%)	3 (75%)
Unknown	0	23	82	25	30	12	32
Physical_activity	0 (0%)	2 (6%)	7 (11%)	0 (0%)	1 (6%)	3 (15%)	3 (17%)	0.069
Unknown	0	39	59	33	28	1	18
Travel_period								0.056
3 months	0 (NA%)	0 (0%)	10 (28%)	0 (NA%)	1 (11%)	5 (26%)	2 (13%)
6 months	0 (NA%)	0 (0%)	8 (22%)	0 (NA%)	1 (11%)	0 (0%)	1 (6%)
Month	0 (NA%)	0 (0%)	2 (6%)	0 (NA%)	1 (11%)	4 (21%)	6 (38%)
Year	0 (NA%)	6 (100%)	16 (44%)	0 (NA%)	6 (67%)	10 (53%)	7 (44%)
Unknown	46	64	85	41	37	2	20
Education_level								0.2
Bachelor's level	0 (NA%)	1 (20%)	11 (31%)	0 (NA%)	3 (33%)	6 (32%)	10 (63%)
Master's level	0 (NA%)	1 (20%)	10 (29%)	0 (NA%)	4 (44%)	10 (53%)	3 (19%)
School Level	0 (NA%)	3 (60%)	14 (40%)	0 (NA%)	2 (22%)	3 (16%)	3 (19%)
Unknown	46	65	86	41	37	2	20
Cosmetics								0.7
Never	0 (NA%)	3 (50%)	18 (50%)	0 (NA%)	4 (50%)	12 (60%)	7 (44%)
Occasionally (a few-8 times/month)	0 (NA%)	2 (33%)	6 (17%)	0 (NA%)	3 (38%)	2 (10%)	4 (25%)
Regularly (3-7 times/week)	0 (NA%)	1 (17%)	12 (33%)	0 (NA%)	1 (13%)	6 (30%)	5 (31%)
Unknown	46	64	85	41	38	1	20
Sleep_duration								0.2
> 8 hours	0 (NA%)	2 (33%)	2 (6%)	0 (NA%)	1 (11%)	3 (15%)	1 (6%)
4-6 hours	0 (NA%)	0 (0%)	2 (6%)	0 (NA%)	1 (11%)	4 (20%)	2 (13%)
6-7 hours	0 (NA%)	2 (33%)	12 (33%)	0 (NA%)	1 (11%)	9 (45%)	7 (44%)
7-8 hours	0 (NA%)	2 (33%)	20 (56%)	0 (NA%)	6 (67%)	4 (20%)	6 (38%)
Unknown	46	64	85	41	37	1	20
BMI_category								0.033
normal/overweight	46 (100%)	64 (98%)	80 (98%)	41 (100%)	39 (98%)	8 (80%)	9 (100%)
obese	0 (0%)	1 (2%)	2 (2%)	0 (0%)	0 (0%)	2 (20%)	0 (0%)
underweight	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (3%)	0 (0%)	0 (0%)
Unknown	0	5	39	0	6	11	27
1 n (%); Median (IQR)
2 Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates); Kruskal-Wallis rank sum test

kMeans

kmeans <- G_scaled %>% 
  kmeans(centers = clusters_number,
         iter.max = 20, nstart = 35) 

  fviz_cluster(kmeans,data = G_scaled,
               ellipse = FALSE,
               show.clust.cent = FALSE,
               geom = "point",
               main = "kMeans clustering based on G-taxa abundance before batch effect correction")

Batch-effects correction

the MBECS package was used, a step-by-step actions description is available at the link https://github.com/rmolbrich/MBECS

Preliminary report is available in /data/originals/.

• Correction of research_ID batch-effect

#Run corrections
mbec.obj <- mbecRunCorrections(
  mbec.obj, 
  model.vars=c('research_ID', 'Health_state'),
#model.vars=c (batch effect, presumed biological effect of interest) 
method = "bat", #batch effect correcting algorithm:
#Batch Mean Centering (bmc) /ComBat (bat)/Remove Batch Effect (rbe)
type = "otu") #which abundance matrix to use

## Found 459 genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.

#Post report
#mbecReportPost(
  #input.obj=mbec.obj, 
  #model.vars=c('research_ID', 'Health_state'), #model.vars=c (batch effect, presumed biological effect of interest) 
#type="otu", # which abundance matrix to use for evaluation: clr/otu/tss
#file.name = "Batch_(research_id)_Correction_Report_otu_bat",
#file.dir = "data/originals/",
#return.data = FALSE)

library (datawizard)

#Retrieve corrrected data
ps.cor.bat <- mbecGetPhyloseq(mbec.obj, 
                          type="cor",
                          label="bat") #which type of data to add (correction)

combined_bacteria_batched <- ps.cor.bat@otu_table %>% 
  as.data.frame() %>% 
  data_rotate (rownames = "patient_ID") %>% 
  mutate (patient_ID = sub ("S", "", patient_ID),
          patient_ID = as.numeric(patient_ID))

data_wide_batched <- combined_info %>% 
  select (where (function(x) 
      sum (!is.na(x))/ nrow(.) * 100 >= 5)) %>% #selecting of variables with less than 95% NA
  left_join(combined_bacteria_batched)

rm (combined_bacteria_batched)

####Principal Component Analysis

• Principal Component Analysis до коррекции batch-эффекта (research_ID)

plot <- mbecPCA(input.obj=mbec.obj, 
        model.vars=c('research_ID', 'Health_state'),
        type="otu", pca.axes=c(1,2), return.data = FALSE) 

ggsave("data/pictures/PCA_befor_batching.jpeg", plot = plot)

• Principal Component Analysis после коррекции batch-эффекта (research_ID)

plot <- mbecPCA(input.obj=mbec.obj, 
        model.vars=c('research_ID', 'Health_state'),
        type="cor", label = "bat",
        pca.axes=c(1,2), return.data = FALSE)  

ggsave("data/pictures/PCA_after_batching.jpeg", plot = plot)

####Heatmap

• Heatmap содержания 10-ти наиболее вариабельных таксонов до коррекции batch-эффекта (research_ID)

mbecHeat(input.obj=mbec.obj, method = "TOP", n = 10, 
         model.vars=c('research_ID', 'Health_state'),
         center = TRUE, scale = TRUE, 
         type="otu",
         return.data = FALSE)

• Heatmap содержания 10-ти наиболее вариабельных таксонов после коррекции batch-эффекта (research_ID)

mbecHeat(input.obj=mbec.obj, method = "TOP", n = 10, 
         model.vars=c('research_ID', 'Health_state'),
         center = TRUE, scale = TRUE, 
         type="cor", label = "bat", 
         return.data = FALSE)

Кластеризация родов (G-таксонов) после коррекции batch-эффекта (research_ID)

AGNES (agglomerative clustering)

clusters_number <- 4 #количество кластеров

library(cluster)
library(factoextra)

G_scaled <- data_wide_batched %>% 
  mutate(patient_ID = paste0("patient_", patient_ID)) %>% 
  column_to_rownames("patient_ID") %>% 
  select (ends_with("_G")) %>% #выбираем только G-таксоны
  scale () #нормируем данные

agnes <- G_scaled %>% 
  dist (method = "euclidean") %>% #cоздаём матрицу дистанций
  as.matrix() %>%  
  agnes( #выбираем Agglomerative clustering (иерархическая кластеризация снизу вверх)
    diss = TRUE, #на вход подана dissimilarity matrix
    method = "ward")

fviz_dend(agnes,
          k = clusters_number,
          rect = TRUE,
          k_colors = "jco",
          show_labels = TRUE,
          label_cols = ifelse (data_wide_batched[agnes$order,]$Health_state == "Health", "green", "red"),#цвет лейблов определяется по Health_state
          cex = 0.2, #размер шрифта лейблов
          main = "Кластеризация субъектов по содержанию G-таксонов после batch-коррекции",
          ylab = "Высота")

• Сравнение полученных кластеров по основным переменным

data_wide %>% 
  add_column("Cluster" = cutree(agnes, k = clusters_number)) %>% # Создаём новый столбец - вектор принадлежности к кластерам  
  select(any_of(colnames (combined_info)), Cluster, - patient_ID) %>% 
  tbl_summary(digits = list(all_continuous() ~ c(0, 0),
                            all_categorical() ~ c(0, 0)),
              by = Cluster) %>% 
  add_p()

Characteristic	1, N = 461	2, N = 2881	3, N = 221	4, N = 251	p-value2
research_ID
1	46 (100%)	0 (0%)	0 (0%)	0 (0%)
2	0 (0%)	35 (12%)	1 (5%)	0 (0%)
3	0 (0%)	66 (23%)	4 (18%)	0 (0%)
4	0 (0%)	69 (24%)	16 (73%)	2 (8%)
5	0 (0%)	2 (1%)	0 (0%)	23 (92%)
6	0 (0%)	22 (8%)	0 (0%)	0 (0%)
7	0 (0%)	94 (33%)	1 (5%)	0 (0%)
Seq_region
V3-V4	0 (0%)	101 (35%)	5 (23%)	0 (0%)
V4	0 (0%)	187 (65%)	17 (77%)	25 (100%)
V5-V6	46 (100%)	0 (0%)	0 (0%)	0 (0%)
Seq_date
2015	0 (0%)	94 (33%)	1 (5%)	0 (0%)
2017	0 (0%)	2 (1%)	0 (0%)	23 (92%)
2018	46 (100%)	43 (15%)	6 (27%)	1 (4%)
2020	0 (0%)	36 (13%)	2 (9%)	1 (4%)
2021	0 (0%)	23 (8%)	9 (41%)	0 (0%)
2022	0 (0%)	23 (8%)	0 (0%)	0 (0%)
2023	0 (0%)	67 (23%)	4 (18%)	0 (0%)
Health_state					<0.001
Disease	0 (0%)	137 (48%)	9 (41%)	24 (96%)
Health	46 (100%)	151 (52%)	13 (59%)	1 (4%)
Age	NA (NA, NA)	32 (27, 46)	48 (42, 51)	34 (28, 48)	0.010
Unknown	46	195	6	0
Age_range					<0.001
> 43	0 (0%)	24 (12%)	11 (52%)	9 (36%)
16-43	46 (100%)	170 (88%)	10 (48%)	16 (64%)
Unknown	0	94	1	0
Sex					0.11
female	0 (NA%)	60 (41%)	5 (26%)	14 (58%)
male	0 (NA%)	88 (59%)	14 (74%)	10 (42%)
Unknown	46	140	3	1
Country
Australia	0 (0%)	3 (1%)	3 (14%)	0 (0%)
Austria	0 (0%)	2 (1%)	2 (9%)	24 (96%)
Belgium	46 (100%)	0 (0%)	0 (0%)	0 (0%)
Germany	0 (0%)	45 (16%)	0 (0%)	0 (0%)
Greece	0 (0%)	1 (0%)	0 (0%)	0 (0%)
Hungary	0 (0%)	0 (0%)	2 (9%)	0 (0%)
Ireland	0 (0%)	0 (0%)	1 (5%)	0 (0%)
Israel	0 (0%)	23 (8%)	0 (0%)	0 (0%)
Italy	0 (0%)	35 (12%)	2 (9%)	0 (0%)
Norway	0 (0%)	1 (0%)	0 (0%)	0 (0%)
Poland	0 (0%)	66 (23%)	4 (18%)	0 (0%)
Spain	0 (0%)	94 (33%)	1 (5%)	0 (0%)
UK	0 (0%)	8 (3%)	3 (14%)	1 (4%)
USA	0 (0%)	10 (3%)	4 (18%)	0 (0%)
Race					0.029
African American	0 (0%)	1 (2%)	0 (0%)	0 (0%)
Asian or Pacific Islander	0 (0%)	1 (2%)	0 (0%)	0 (0%)
Caucasian	46 (100%)	52 (79%)	16 (100%)	2 (100%)
Hispanic	0 (0%)	1 (2%)	0 (0%)	0 (0%)
Other	0 (0%)	11 (17%)	0 (0%)	0 (0%)
Unknown	0	222	6	23
Smoking	0 (0%)	15 (11%)	1 (5%)	0 (0%)	0.060
Unknown	0	153	2	23
Alcohol					>0.9
Never	0 (NA%)	10 (14%)	2 (13%)	0 (0%)
Occasionally (1-2 times/week)	0 (NA%)	20 (29%)	4 (25%)	0 (0%)
Rarely (a few times/month)	0 (NA%)	26 (38%)	8 (50%)	2 (100%)
Regularly (3-7 times/week)	0 (NA%)	13 (19%)	2 (13%)	0 (0%)
Unknown	46	219	6	23
Antibiotics_usage					<0.001
1-6 months	46 (100%)	1 (1%)	1 (9%)	0 (0%)
12 months/Not use	0 (0%)	118 (99%)	10 (91%)	1 (100%)
Unknown	0	169	11	24
Physical_activity	0 (0%)	15 (11%)	1 (5%)	0 (0%)	0.060
Unknown	0	153	2	23
Travel_period					0.016
3 months	0 (NA%)	13 (19%)	5 (33%)	0 (0%)
6 months	0 (NA%)	10 (14%)	0 (0%)	0 (0%)
Month	0 (NA%)	7 (10%)	4 (27%)	2 (100%)
Year	0 (NA%)	39 (57%)	6 (40%)	0 (0%)
Unknown	46	219	7	23
Education_level					0.070
Bachelor's level	0 (NA%)	26 (39%)	5 (33%)	0 (0%)
Master's level	0 (NA%)	18 (27%)	8 (53%)	2 (100%)
School Level	0 (NA%)	23 (34%)	2 (13%)	0 (0%)
Unknown	46	221	7	23
Cosmetics					0.4
Never	0 (NA%)	32 (47%)	11 (69%)	1 (50%)
Occasionally (a few-8 times/month)	0 (NA%)	16 (24%)	1 (6%)	0 (0%)
Regularly (3-7 times/week)	0 (NA%)	20 (29%)	4 (25%)	1 (50%)
Unknown	46	220	6	23
Sleep_duration					0.14
> 8 hours	0 (NA%)	7 (10%)	2 (13%)	0 (0%)
4-6 hours	0 (NA%)	5 (7%)	4 (25%)	0 (0%)
6-7 hours	0 (NA%)	23 (33%)	7 (44%)	1 (50%)
7-8 hours	0 (NA%)	34 (49%)	3 (19%)	1 (50%)
Unknown	46	219	6	23
BMI_category					0.063
normal/overweight	46 (100%)	229 (98%)	11 (85%)	1 (100%)
obese	0 (0%)	3 (1%)	2 (15%)	0 (0%)
underweight	0 (0%)	1 (0%)	0 (0%)	0 (0%)
Unknown	0	55	9	24
1 n (%); Median (IQR)
2 Pearson’s Chi-squared test; Kruskal-Wallis rank sum test; Fisher’s exact test

kMeans

kmeans <- G_scaled %>% 
  kmeans(centers = clusters_number, # Кол-во кластеров
         iter.max = 20, # Максимальное кол-во итераций
         nstart = 35) # Кол-во центройдов в начале

  fviz_cluster(kmeans,data = G_scaled,
               ellipse = FALSE,
               show.clust.cent = FALSE,
               geom = "point",
               main = "Кластеризация субъектов по содержанию G-таксонов до коррекции batch-эффекта")

AGNES (несходство Брея — Кертиса)

• с матрицей-дистанций с показателями несходства Брея — Кертиса

agnes2 <- data_wide_batched %>% 
  column_to_rownames("patient_ID") %>% 
  select (ends_with("_G")) %>% 
  vegdist(method = "bray") %>% #cоздаём матрицу дистанций, рассчитывая показатель несходства Брея — Кертиса (Bray-Curtis dissimilarity, 0 - полное совпадение пациентов по составу микробиоты, 1 - полное несовпадение 
  as.matrix() %>% 
  agnes( #выбираем Agglomerative clustering (иерархическая кластеризация снизу вверх)
    diss = TRUE, #на вход подана dissimilarity matrix
    method = "ward")

fviz_dend(agnes2, 
          cex = 0.2, k = clusters_number,
          rect = TRUE,
          k_colors = "jco",
          color_labels_by_k = FALSE,
          label_cols = case_when (data_wide_batched[agnes2$order,]$Age_range == "> 43" ~ "red",
                                  data_wide_batched[agnes2$order,]$Age_range == "16-43" ~ "green",
                                  TRUE ~ "white"),
          main = "Дендрограмма кластеризации субъектов по количеству G-таксонов",
          ylab = "Высота")

• Сравнение полученных кластеров по основным переменным

data_wide %>% 
  add_column("Cluster" = cutree(agnes2, k = clusters_number)) %>% # Создаём новый столбец - вектор принадлежности к кластерам  
  select(any_of(colnames (combined_info)), Cluster, - patient_ID) %>% 
  tbl_summary(digits = list(all_continuous() ~ c(0, 0),
                            all_categorical() ~ c(0, 0)),
              by = Cluster) %>% 
  add_p()

Characteristic	1, N = 2091	2, N = 401	3, N = 581	4, N = 741	p-value2
research_ID
1	25 (12%)	3 (8%)	11 (19%)	7 (9%)
2	20 (10%)	1 (3%)	3 (5%)	12 (16%)
3	43 (21%)	3 (8%)	11 (19%)	13 (18%)
4	47 (22%)	16 (40%)	9 (16%)	15 (20%)
5	12 (6%)	9 (23%)	2 (3%)	2 (3%)
6	13 (6%)	3 (8%)	4 (7%)	2 (3%)
7	49 (23%)	5 (13%)	18 (31%)	23 (31%)
Seq_region
V3-V4	63 (30%)	4 (10%)	14 (24%)	25 (34%)
V4	121 (58%)	33 (83%)	33 (57%)	42 (57%)
V5-V6	25 (12%)	3 (8%)	11 (19%)	7 (9%)
Seq_date
2015	49 (23%)	5 (13%)	18 (31%)	23 (31%)
2017	12 (6%)	9 (23%)	2 (3%)	2 (3%)
2018	50 (24%)	8 (20%)	16 (28%)	22 (30%)
2020	24 (11%)	3 (8%)	5 (9%)	7 (9%)
2021	16 (8%)	9 (23%)	2 (3%)	5 (7%)
2022	14 (7%)	3 (8%)	4 (7%)	2 (3%)
2023	44 (21%)	3 (8%)	11 (19%)	13 (18%)
Health_state					0.3
Disease	88 (42%)	23 (58%)	28 (48%)	31 (42%)
Health	121 (58%)	17 (43%)	30 (52%)	43 (58%)
Age	35 (28, 47)	38 (31, 49)	32 (28, 41)	28 (26, 46)	0.5
Unknown	137	12	43	55
Age_range					0.002
> 43	21 (13%)	13 (37%)	4 (10%)	6 (12%)
16-43	139 (87%)	22 (63%)	36 (90%)	45 (88%)
Unknown	49	5	18	23
Sex					0.015
female	54 (51%)	9 (31%)	8 (35%)	8 (24%)
male	51 (49%)	20 (69%)	15 (65%)	26 (76%)
Unknown	104	11	35	40
Country
Australia	2 (1%)	3 (8%)	0 (0%)	1 (1%)
Austria	12 (6%)	12 (30%)	2 (3%)	2 (3%)
Belgium	25 (12%)	3 (8%)	11 (19%)	7 (9%)
Germany	29 (14%)	1 (3%)	5 (9%)	10 (14%)
Greece	1 (0%)	0 (0%)	0 (0%)	0 (0%)
Hungary	0 (0%)	2 (5%)	0 (0%)	0 (0%)
Ireland	1 (0%)	0 (0%)	0 (0%)	0 (0%)
Israel	14 (7%)	3 (8%)	4 (7%)	2 (3%)
Italy	20 (10%)	2 (5%)	3 (5%)	12 (16%)
Norway	0 (0%)	0 (0%)	0 (0%)	1 (1%)
Poland	43 (21%)	3 (8%)	11 (19%)	13 (18%)
Spain	49 (23%)	5 (13%)	18 (31%)	23 (31%)
UK	2 (1%)	4 (10%)	4 (7%)	2 (3%)
USA	11 (5%)	2 (5%)	0 (0%)	1 (1%)
Race					0.6
African American	1 (1%)	0 (0%)	0 (0%)	0 (0%)
Asian or Pacific Islander	0 (0%)	0 (0%)	1 (5%)	0 (0%)
Caucasian	60 (87%)	19 (100%)	18 (90%)	19 (86%)
Hispanic	1 (1%)	0 (0%)	0 (0%)	0 (0%)
Other	7 (10%)	0 (0%)	1 (5%)	3 (14%)
Unknown	140	21	38	52
Smoking	8 (7%)	2 (9%)	1 (3%)	5 (14%)	0.4
Unknown	94	18	27	39
Alcohol					0.9
Never	8 (17%)	2 (13%)	1 (11%)	1 (7%)
Occasionally (1-2 times/week)	15 (32%)	3 (19%)	1 (11%)	5 (33%)
Rarely (a few times/month)	17 (36%)	8 (50%)	4 (44%)	7 (47%)
Regularly (3-7 times/week)	7 (15%)	3 (19%)	3 (33%)	2 (13%)
Unknown	162	24	49	59
Antibiotics_usage					0.2
1-6 months	25 (25%)	5 (42%)	11 (38%)	7 (19%)
12 months/Not use	75 (75%)	7 (58%)	18 (62%)	29 (81%)
Unknown	109	28	29	38
Physical_activity	8 (7%)	2 (9%)	1 (3%)	5 (14%)	0.4
Unknown	94	18	27	39
Travel_period					0.2
3 months	11 (23%)	4 (27%)	1 (11%)	2 (13%)
6 months	5 (11%)	0 (0%)	1 (11%)	4 (27%)
Month	7 (15%)	5 (33%)	1 (11%)	0 (0%)
Year	24 (51%)	6 (40%)	6 (67%)	9 (60%)
Unknown	162	25	49	59
Education_level					0.8
Bachelor's level	18 (38%)	5 (33%)	3 (33%)	5 (38%)
Master's level	14 (30%)	7 (47%)	4 (44%)	3 (23%)
School Level	15 (32%)	3 (20%)	2 (22%)	5 (38%)
Unknown	162	25	49	61
Cosmetics					0.10
Never	19 (40%)	12 (75%)	4 (50%)	9 (60%)
Occasionally (a few-8 times/month)	11 (23%)	0 (0%)	3 (38%)	3 (20%)
Regularly (3-7 times/week)	17 (36%)	4 (25%)	1 (13%)	3 (20%)
Unknown	162	24	50	59
Sleep_duration					0.092
> 8 hours	4 (9%)	2 (13%)	1 (11%)	2 (13%)
4-6 hours	2 (4%)	5 (31%)	1 (11%)	1 (7%)
6-7 hours	19 (40%)	6 (38%)	1 (11%)	5 (33%)
7-8 hours	22 (47%)	3 (19%)	6 (67%)	7 (47%)
Unknown	162	24	49	59
BMI_category					0.5
normal/overweight	158 (98%)	20 (100%)	49 (98%)	60 (98%)
obese	4 (2%)	0 (0%)	0 (0%)	1 (2%)
underweight	0 (0%)	0 (0%)	1 (2%)	0 (0%)
Unknown	47	20	8	13
1 n (%); Median (IQR)
2 Pearson’s Chi-squared test; Kruskal-Wallis rank sum test; Fisher’s exact test

data_wide (batched)

`

data_wide <- data_wide_batched  

write_rds(data_wide, 
          file = "data/originals/data_wide.rds")

bac_functions dataset

# Чтение листов Excel-файла с функциями бактерий и их объединение
path <- "data/raw/Bacterial group functions.xlsx"
taxon <- c ("TaxonName", "Rank")

neuromediators <- read_xlsx (path, 2) %>% 
  mutate(Destroy = ifelse(is.na (Destroy), "produce", "destroy")) %>% 
  unique() %>%
  pivot_wider(names_from = Neuromediator, values_from = Destroy)

probiotics <- read_xlsx (path, 3) %>% 
  add_column(probiotics = 1)

special_properties <- read_xlsx (path, 4) %>% 
  add_column(special_properties = 1)

vitamins <- read_xlsx (path, 5) %>% 
  pivot_wider (names_from = Vitamin, values_from = Vitamin,
               values_fn = function(x) ifelse(is.na (x), NA, 1))

habbits <- read_xlsx (path, 7) %>%
  unique() %>% #удаление повторяющихся строк 
  pivot_wider(names_from = Habbit, values_from = Habit_state)

bac_functions <- read_xlsx (path, 1) %>% #Патогены и нежелательные
  full_join(neuromediators, by = taxon) %>% #Нейромедиаторы
  full_join(probiotics, by = taxon) %>% #Пробиотики
  full_join(special_properties, by = taxon) %>% #С особыми свойствами
  full_join(vitamins, by = taxon) %>%  #Витамины
  full_join(read_xlsx (path, 6), by = taxon) %>% #Продуценты КЦДК
  full_join(habbits, by = taxon) %>% #Вредные привычки
  
  unite("Taxon", TaxonName, Rank, sep = "_") %>% 
  filter (Taxon != "Blautia obeum_S") %>% #для данного таксона противоречивая информация в Продуценты КЦЖК
  mutate_all(as.factor)

rm (path, taxon, neuromediators, probiotics, special_properties, vitamins, habbits)

data_long

• Перевод data_wide в длинный формат (data_long), объединение data_long и bac_functions

# Создание датасета в длинном формате
data_long <- data_wide %>% 
  pivot_longer(ends_with(c("_D", "_P", "_O", "_C", "_F", "_G")),
               names_to = "Taxon", values_to = "Percentage")

#Перезапись data_long с добавлением функций бактерий
data_long <- data_long %>% 
  left_join (bac_functions, by = "Taxon")

#Сохранение data_long.rds
write_rds(data_long, 
          file = "data/originals/data_long.rds",
          compress = "gz") 

• Создание отдельных длинных датасетов для каждого таксона

G_long <- data_long %>% subset(grepl("_G", Taxon)) 
# %>% select (where (function(x) sum (is.na(x))/ nrow(.) * 100 < threshold_percent))
F_long <- data_long %>% subset(grepl("_F", Taxon))
C_long <- data_long %>% subset(grepl("_C", Taxon))
O_long <- data_long %>% subset(grepl("_O", Taxon))
P_long <- data_long %>% subset(grepl("_P", Taxon))

write_rds(G_long, 
          file = "data/originals/G_long.rds", "gz") 
write_rds(F_long, 
          file = "data/originals/F_long.rds", "gz") 
write_rds(C_long, 
          file = "data/originals/C_long.rds", "gz") 
write_rds(O_long, 
          file = "data/originals/O_long.rds", "gz") 
write_rds(P_long, 
          file = "data/originals/P_long.rds", "gz") 

Анализ

Сравнение среднего общего содержания G_таксонов с особыми свойствами

comparison <- function (special_data, special_property) {
#vector of the G_taxa names with special properties:  
    bac_names <-  special_data %>% 
      subset(grepl("_G", Taxon)) %>% .$Taxon
#number of the G_taxa in data_wide
    number_of_taxons <- data_wide %>% 
      select(patient_ID, Health_state, any_of(bac_names)) %>% 
      ncol() - 2
# t-test
t <- data_wide %>% 
  select(patient_ID, Health_state, any_of(bac_names)) %>% 
  pivot_longer(ends_with("_G"),
               names_to = "Taxon", values_to = "Percentage") %>% 
  group_by(patient_ID, Health_state) %>% 
  summarise(across(Percentage, sum)) %>% 
  ungroup() %>% 
  t.test (Percentage ~ Health_state, .)
#a tibble with results
tibble("G_taxa" = special_property, 
       "Number of taxa" = number_of_taxons,
       "Mean of total percentage in IBS" = t$estimate["mean in group Disease"],
       "Mean of total percentage in healthy" = t$estimate["mean in group Health"],
       "95% CI_1" = t$conf.int[1],
       "95% CI_2" = t$conf.int[2],
       "p.unadjusted" = t$p.value)
}


comparison_in_total <- rbind(
    comparison (bac_functions %>% filter (Серотонин == "produce"), "Serotonin producing"),
    comparison (bac_functions %>% filter (Серотонин == "destroy"), "Serotonin destroying"),
    comparison (bac_functions %>% filter (Ацетилхолин == "produce"), "Acetylcholine producing"),
    comparison (bac_functions %>% filter (ГАМК == "produce"), "GABA producing"),
    comparison (bac_functions %>% filter (ГАМК == "destroy"), "GABA destroying"),
    comparison (bac_functions %>% filter (Дофамин == "produce"), "Dopamine producing"),
    comparison (bac_functions %>% filter (Дофамин == "destroy"), "Dopamine destroying"),
    comparison (bac_functions %>% filter (probiotics == 1), "Probiotics"),
    comparison (bac_functions %>% filter (special_properties == 1), "Special_properties"),
    comparison (bac_functions %>% filter (Gases == 1), "Gases producing"),
    comparison (bac_functions %>% filter (Oral == 1), "Oral"),
    comparison (bac_functions %>% filter (Inflammatory == 1), "Inflammatory"),
    comparison (bac_functions %>% filter (Bacteria_category == "Патоген"), "Pathogens"),
    comparison (bac_functions %>% filter (Bacteria_category == "Условно-нормальный"), "Conditionally normal"),
    comparison (bac_functions %>% filter (A == 1), "Vitamin A producing"),
    comparison (bac_functions %>% filter (B1 == 1), "Vitamin B1 producing"),
    comparison (bac_functions %>% filter (B12 == 1), "Vitamin B12 producing"),
    comparison (bac_functions %>% filter (B2 == 1), "Vitamin B2 producing"),
    comparison (bac_functions %>% filter (B3 == 1), "Vitamin B3 producing"),
    comparison (bac_functions %>% filter (B5 == 1), "Vitamin B5 producing"),
    comparison (bac_functions %>% filter (B6 == 1), "Vitamin B6 producing"),
    comparison (bac_functions %>% filter (B7 == 1), "Vitamin B7 producing"),
    comparison (bac_functions %>% filter (B9 == 1), "Vitamin B9 producing"),
    comparison (bac_functions %>% filter (D3 == 1), "Vitamin D3 producing"),
    comparison (bac_functions %>% filter (K2 == 1), "Vitamin K2 producing"),
    comparison (bac_functions %>% filter (Ацетат == 1), "Acetate producing"),
    comparison (bac_functions %>% filter (Пропионат == 1), "Propionate producing"),
    comparison (bac_functions %>% filter (`Масляная кислота` == 1), "Butyrate producing"),
    comparison (bac_functions %>% filter (Кофе == "Увеличена"), "Increase with coffee"),
    comparison (bac_functions %>% filter (Курение == "Увеличена"), "Increase with smoking"),
    comparison (bac_functions %>% filter (Курение == "Уменьшена"), "Derease with smoking"),
    comparison (bac_functions %>% filter (Алкоголь == "Увеличена"), "Increase with alcohol"),
    comparison (bac_functions %>% filter (Алкоголь == "Уменьшена"), "Derease with alcohol")
    ) %>% 
  filter (`Number of taxa` >= 3) %>% 
  add_column(.before = "95% CI_1", "Difference in means" = .$"Mean of total percentage in IBS" - .$"Mean of total percentage in healthy") %>% 
  mutate (across (c ("Mean of total percentage in IBS", "Mean of total percentage in healthy", "Difference in means", "95% CI_1", "95% CI_2"), function (x) round (x,2)),
          p.unadjusted = round (p.unadjusted, 3),
          p.value.holm = p.adjust(p.unadjusted, "holm")) %>% 
  unite("95% CI_1", "95% CI_2", col = "95% CI", sep = ":") %>% 
  arrange(desc (abs (.$"Difference in means")))

write_rds(comparison_in_total, 
          file = "data/originals/comparison_in_total.rds") 

comparison_in_total %>% 
  mutate (p.unadjusted = ifelse(p.unadjusted < 0.001, "< 0.001", p.unadjusted),
          p.value.holm = ifelse(p.value.holm < 0.001, "< 0.001", p.value.holm)) %>%
  flextable() %>% theme_box() %>% 
  align(align = "center", part = "all") %>% 
  flextable::footnote (i = 1, j = 7, value = as_paragraph (c ("Welch Two Sample t-test")),
            ref_symbols = "1", part = "header") %>% 
  set_caption("Results of IBS/healthy people comparison of means of total percentage of G-taxa with special properties")

Results of IBS/healthy people comparison of means of total percentage of G-taxa with special properties

G_taxa	Number of taxa	Mean of total percentage in IBS	Mean of total percentage in healthy	Difference in means	95% CI	p.unadjusted1	p.value.holm
Inflammatory	48	5.34	3.00	2.34	1.65:3.03	< 0.001	< 0.001
Propionate producing	18	22.95	20.66	2.29	0.11:4.48	0.04	0.8
Acetate producing	31	26.19	24.30	1.89	-0.31:4.08	0.092	1
Increase with alcohol	3	15.25	13.38	1.87	-0.14:3.87	0.068	1
Increase with coffee	5	7.71	9.49	-1.78	-2.66:-0.89	< 0.001	< 0.001
Vitamin B12 producing	4	4.69	6.23	-1.54	-2.35:-0.74	< 0.001	< 0.001
Pathogens	7	1.31	0.07	1.24	0.91:1.57	< 0.001	< 0.001
Special_properties	34	11.12	12.28	-1.16	-2.15:-0.17	0.022	0.484
Conditionally normal	50	4.37	3.27	1.10	0.43:1.77	0.001	0.023
Gases producing	9	2.77	2.01	0.76	0.38:1.14	< 0.001	< 0.001
Serotonin destroying	7	2.97	2.25	0.72	0.09:1.36	0.026	0.546
Probiotics	5	1.97	2.48	-0.51	-1.15:0.12	0.115	1
Vitamin B9 producing	9	3.34	3.83	-0.50	-1.14:0.15	0.131	1
Serotonin producing	20	5.75	6.20	-0.45	-1.24:0.34	0.266	1
Increase with smoking	10	2.47	2.02	0.45	-0.1:1	0.11	1
GABA producing	6	2.82	3.27	-0.44	-1.09:0.2	0.175	1
Vitamin D3 producing	4	2.90	3.28	-0.38	-1.04:0.28	0.257	1
Vitamin A producing	4	2.77	3.13	-0.36	-1.02:0.29	0.279	1
Vitamin B1 producing	7	3.00	3.36	-0.36	-1.04:0.31	0.292	1
Vitamin B5 producing	13	3.90	3.57	0.33	-0.13:0.79	0.159	1
Vitamin B2 producing	29	7.46	7.73	-0.27	-1.11:0.57	0.534	1
Derease with smoking	4	3.52	3.34	0.18	-0.52:0.88	0.621	1
Butyrate producing	34	25.06	25.16	-0.11	-2.17:1.95	0.919	1
Vitamin B3 producing	16	6.90	7.00	-0.10	-0.92:0.72	0.811	1
Vitamin B6 producing	25	5.90	5.81	0.09	-0.71:0.89	0.829	1
Vitamin B7 producing	14	3.01	3.09	-0.08	-0.58:0.42	0.75	1
Oral	18	0.80	0.86	-0.07	-0.33:0.19	0.612	1
Vitamin K2 producing	3	0.03	0.03	0.00	-0.02:0.01	0.631	1
1Welch Two Sample t-test

Факторный анализ

• Проведем множественное сравнение каждого таксона из combined_bacteria с по группе Health_state из combined_info без учета таксономического уровня.

# Создание нового датасета для сохранения результатов
result_dataset <- data.frame(
  Variable_Name = character(),
  Test_Type = character(),
  P_Value = numeric(),
  Normal_Distribution = character(),
  stringsAsFactors = FALSE
)

alpha = 0.05

# Объединяем датасеты по patient_id 
combined_data <- data_wide %>%
  select(Health_state,
    ends_with(c("_D", "_P", "_O", "_C", "_F", "_G")))

combined_bacteria_clean <- combined_data

# Получаем список переменных из датасета combined_bacteria, исключая "patient_ID"
values_to_exclude <- c("patient_ID", "Seq_date", "Age")
variable_names <- setdiff(names(combined_bacteria_clean), values_to_exclude)

# Проходим по каждой переменной
for (variable in variable_names) {
  # Фильтрация данных (исключаем строки с NA и 0 в текущей переменной)
  filtered_data <- combined_data[!is.na(combined_data[[variable]]) & combined_data[[variable]] != 0, ]
  #print(variable)
  #print(filtered_data)

  # Проверим что датасет filtered_data не пустой и что количество групп сравнения более 1, в нашем случае их 2 :)
  if (nrow(filtered_data) > 0 & length(unique(filtered_data$Health_state)) > 1) {
    
  # Check if there is sufficient variability in the data
  if (length(unique(filtered_data$Health_state)) > 1) {
    # Проверка на нормальность
    shapiro_test_result <- try(shapiro.test(filtered_data[[variable]]), silent = TRUE)
    if (inherits(shapiro_test_result, "try-error")) {
      warning(paste("Skipping Shapiro-Wilk test for variable", variable, "due to an error."))
      next
    }
    p_value_shapiro <- shapiro_test_result$p.value

    # Выбор соответствующего статистического теста
    if (p_value_shapiro > 0.05) {
      # Если нормальное распределение, провести дисперсионный анализ
      model <- aov(filtered_data[[variable]] ~ Health_state, data = filtered_data)
      summary_list <- summary(model)
      test_type <- "ANOVA"
    } else {
      # Если не нормальное распределение, использовать тест Краскела-Уоллиса
      tryCatch({
        model <- kruskal.test(filtered_data[[variable]] ~ Health_state, data = filtered_data)
        test_type <- "Kruskal-Wallis"
      }, error = function(e) {
        warning(paste("Skipping Kruskal-Wallis test for variable", variable, "due to an error:", conditionMessage(e)))
        next
      })
    }

    # Добавление результатов выбранного теста в датасет
    result_dataset <- rbind(result_dataset, 
                            data.frame(Variable_Name = variable,
                                       Test_Type = test_type,
                                       P_Value = ifelse(test_type == "ANOVA", summary_list[[1]]$"Pr(>F)"[1], model$p.value),
                                       Normal_Distribution = ifelse(p_value_shapiro > 0.05, "Yes", "No")))
  } else {
    # If there is no variability, skip the tests
    warning(paste("Skipping tests for variable", variable, "as there is not enough variability in the data."))
  }
  }}

# Добавление столбца с поправкой Бонферрони
result_dataset$Adjusted_P_Value_Bonferroni <- p.adjust(result_dataset$P_Value, method = "bonferroni")

# Добавление столбца с поправкой Холма
result_dataset$Adjusted_P_Value_Holm <- p.adjust(result_dataset$P_Value, method = "holm")

# Добавление столбца с поправкой Бенджамини-Хохберга
result_dataset$Adjusted_P_Value_BH <- p.adjust(result_dataset$P_Value, method = "BH")

result_dataset$test_pass = ifelse(result_dataset$Adjusted_P_Value_Bonferroni < alpha & result_dataset$Adjusted_P_Value_Holm < alpha & result_dataset$Adjusted_P_Value_BH < alpha, "Y", "N")

result_dataset_pass <- result_dataset %>% filter(test_pass == "Y")

# Вывод результатов
print(result_dataset_pass)

##                               Variable_Name      Test_Type      P_Value
## 1                               Eukaryota_D Kruskal-Wallis 3.641890e-07
## 2                         Acidobacteriota_P Kruskal-Wallis 4.931868e-06
## 3                        Actinobacteriota_P Kruskal-Wallis 5.956848e-07
## 4                          Armatimonadota_P Kruskal-Wallis 5.555616e-10
## 5                        Bdellovibrionota_P Kruskal-Wallis 1.102695e-09
## 6                        Campylobacterota_P Kruskal-Wallis 4.120866e-06
## 7                           Cyanobacteria_P Kruskal-Wallis 2.162075e-22
## 8                        Desulfobacterota_P Kruskal-Wallis 2.250375e-08
## 9                          Fibrobacterota_P Kruskal-Wallis 9.167248e-08
## 10                             Firmicutes_P Kruskal-Wallis 3.498944e-16
## 11                         Fusobacteriota_P Kruskal-Wallis 2.304884e-12
## 12                           Nitrospirota_P Kruskal-Wallis 7.764816e-07
## 13                         Proteobacteria_P Kruskal-Wallis 1.121119e-31
## 14                      Verrucomicrobiota_P Kruskal-Wallis 1.976683e-08
## 15                             Bacillales_O Kruskal-Wallis 1.093474e-12
## 16                      Bdellovibrionales_O Kruskal-Wallis 2.180046e-12
## 17                        Burkholderiales_O Kruskal-Wallis 2.874918e-05
## 18                     Clostridia UCG-014_O Kruskal-Wallis 3.732801e-19
## 19             Clostridia vadinBB60 group_O Kruskal-Wallis 3.209141e-28
## 20                          Clostridiales_O Kruskal-Wallis 1.067999e-26
## 21                                 DTU014_O Kruskal-Wallis 1.461310e-07
## 22                   Desulfitobacteriales_O Kruskal-Wallis 1.646832e-05
## 23                      Desulfobacterales_O Kruskal-Wallis 3.271719e-07
## 24                     Desulfovibrionales_O Kruskal-Wallis 4.598530e-08
## 25                       Enterobacterales_O Kruskal-Wallis 3.493634e-43
## 26                        Fibrobacterales_O Kruskal-Wallis 1.952118e-11
## 27                        Fusobacteriales_O Kruskal-Wallis 2.304884e-12
## 28                    Gastranaerophilales_O Kruskal-Wallis 1.784014e-33
## 29                       Izemoplasmatales_O Kruskal-Wallis 3.915759e-32
## 30                        Lactobacillales_O Kruskal-Wallis 1.933468e-09
## 31                             Opitutales_O Kruskal-Wallis 5.646650e-07
## 32                        Oscillospirales_O Kruskal-Wallis 6.906239e-11
## 33                         Pedosphaerales_O Kruskal-Wallis 1.302641e-15
## 34    Peptostreptococcales-Tissierellales_O Kruskal-Wallis 1.840308e-26
## 35                           Pirellulales_O Kruskal-Wallis 3.196572e-09
## 36                    Propionibacteriales_O Kruskal-Wallis 1.267265e-13
## 37                        Pseudomonadales_O Kruskal-Wallis 3.208077e-12
## 38                                   RF39_O Kruskal-Wallis 1.317623e-08
## 39                        Rhodobacterales_O Kruskal-Wallis 9.110302e-22
## 40                       Rhodospirillales_O Kruskal-Wallis 9.132432e-06
## 41                          Rickettsiales_O Kruskal-Wallis 2.020389e-16
## 42                                SBR1031_O Kruskal-Wallis 1.172360e-06
## 43                       Staphylococcales_O Kruskal-Wallis 3.046562e-07
## 44         Veillonellales-Selenomonadales_O Kruskal-Wallis 1.606580e-11
## 45                     Verrucomicrobiales_O Kruskal-Wallis 7.214817e-30
## 46                          Victivallales_O Kruskal-Wallis 1.262006e-43
## 47                        Xanthomonadales_O Kruskal-Wallis 2.298532e-35
## 48                        Bdellovibrionia_C Kruskal-Wallis 1.961073e-08
## 49                        Campylobacteria_C Kruskal-Wallis 4.506978e-06
## 50                           Chloroflexia_C Kruskal-Wallis 1.333103e-52
## 51                             Clostridia_C Kruskal-Wallis 2.869853e-19
## 52                        Dehalococcoidia_C Kruskal-Wallis 1.999580e-05
## 53                     Desulfitobacteriia_C Kruskal-Wallis 1.646832e-05
## 54                        Desulfobacteria_C Kruskal-Wallis 2.570935e-06
## 55                       Desulfovibrionia_C Kruskal-Wallis 4.598530e-08
## 56                          Fibrobacteria_C Kruskal-Wallis 1.952118e-11
## 57                          Fusobacteriia_C Kruskal-Wallis 2.304884e-12
## 58                    Gammaproteobacteria_C Kruskal-Wallis 5.052401e-24
## 59                        Gracilibacteria_C Kruskal-Wallis 3.949566e-09
## 60                          Lentisphaeria_C Kruskal-Wallis 1.437163e-32
## 61                         Microgenomatia_C Kruskal-Wallis 2.221509e-05
## 62                          Negativicutes_C Kruskal-Wallis 1.630876e-10
## 63                          Parcubacteria_C Kruskal-Wallis 4.142854e-11
## 64                          Phycisphaerae_C Kruskal-Wallis 3.006549e-07
## 65                       Vampirivibrionia_C Kruskal-Wallis 1.714190e-17
## 66                       Verrucomicrobiae_C Kruskal-Wallis 5.551747e-20
## 67                              vadinHA49_C Kruskal-Wallis 4.885983e-06
## 68                                    A4b_F Kruskal-Wallis 5.664201e-18
## 69                          Aerococcaceae_F Kruskal-Wallis 1.005536e-10
## 70                        Akkermansiaceae_F Kruskal-Wallis 1.680017e-29
## 71                       Alteromonadaceae_F Kruskal-Wallis 5.510037e-25
## 72                            Bacillaceae_F Kruskal-Wallis 2.467063e-12
## 73                     Bdellovibrionaceae_F Kruskal-Wallis 2.180046e-12
## 74                       Burkholderiaceae_F Kruskal-Wallis 4.981468e-21
## 75                       Caloramatoraceae_F Kruskal-Wallis 2.538328e-21
## 76                      Carnobacteriaceae_F Kruskal-Wallis 1.039051e-05
## 77                         Clostridiaceae_F Kruskal-Wallis 8.902870e-27
## 78                         Comamonadaceae_F Kruskal-Wallis 6.571902e-11
## 79                      Coriobacteriaceae_F Kruskal-Wallis 4.658545e-12
## 80                     Corynebacteriaceae_F Kruskal-Wallis 1.917933e-06
## 81                    Desulfovibrionaceae_F Kruskal-Wallis 9.189183e-09
## 82               Dethiosulfatibacteraceae_F Kruskal-Wallis 3.006610e-48
## 83                     Enterobacteriaceae_F Kruskal-Wallis 2.753246e-45
## 84                            Erwiniaceae_F Kruskal-Wallis 1.426000e-46
## 85                              Family XI_F Kruskal-Wallis 1.732333e-07
## 86                        Fusibacteraceae_F Kruskal-Wallis 1.175304e-12
## 87                       Fusobacteriaceae_F Kruskal-Wallis 1.130697e-06
## 88                            Gemellaceae_F Kruskal-Wallis 3.263173e-17
## 89                       Leptotrichiaceae_F Kruskal-Wallis 3.156349e-20
## 90                ML635J-40 aquatic group_F Kruskal-Wallis 1.343215e-06
## 91                         Micrococcaceae_F Kruskal-Wallis 3.198537e-28
## 92                          Moraxellaceae_F Kruskal-Wallis 3.150003e-27
## 93                         Morganellaceae_F Kruskal-Wallis 2.198451e-15
## 94                          Neisseriaceae_F Kruskal-Wallis 4.802029e-38
## 95                       Oxalobacteraceae_F Kruskal-Wallis 1.372032e-12
## 96                        Pasteurellaceae_F Kruskal-Wallis 1.032882e-24
## 97                        Pedosphaeraceae_F Kruskal-Wallis 1.302641e-15
## 98                  Peptostreptococcaceae_F Kruskal-Wallis 7.392404e-35
## 99                          Pirellulaceae_F Kruskal-Wallis 3.196572e-09
## 100                        Planococcaceae_F Kruskal-Wallis 3.691366e-18
## 101                  Propionibacteriaceae_F Kruskal-Wallis 6.631706e-09
## 102                      Pseudomonadaceae_F Kruskal-Wallis 3.996889e-22
## 103                      Rhodobacteraceae_F Kruskal-Wallis 9.110302e-22
## 104                        Rhodocyclaceae_F Kruskal-Wallis 7.399735e-20
## 105                       Ruminococcaceae_F Kruskal-Wallis 1.582549e-08
## 106                                  SB-5_F Kruskal-Wallis 3.298360e-07
## 107                        Shewanellaceae_F Kruskal-Wallis 5.652343e-08
## 108                        Sutterellaceae_F Kruskal-Wallis 1.095159e-05
## 109                               UCG-010_F Kruskal-Wallis 1.173862e-23
## 110                       Veillonellaceae_F Kruskal-Wallis 2.025402e-12
## 111                          Vibrionaceae_F Kruskal-Wallis 1.133000e-19
## 112                        Victivallaceae_F Kruskal-Wallis 3.983873e-47
## 113                         Weeksellaceae_F Kruskal-Wallis 7.658696e-15
## 114                      Xanthomonadaceae_F Kruskal-Wallis 1.867012e-05
## 115                          Yersiniaceae_F Kruskal-Wallis 1.036265e-37
## 116 [Eubacterium] coprostanoligenes group_F Kruskal-Wallis 1.838722e-27
## 117                   Acetanaerobacterium_G Kruskal-Wallis 7.477478e-29
## 118                       Acidaminococcus_G Kruskal-Wallis 2.000921e-05
## 119                         Acinetobacter_G Kruskal-Wallis 7.548236e-30
## 120                        Actinobacillus_G Kruskal-Wallis 9.404740e-23
## 121                             Aeromonas_G Kruskal-Wallis 5.221036e-06
## 122                           Akkermansia_G Kruskal-Wallis 1.680017e-29
## 123                          Anaerococcus_G Kruskal-Wallis 1.600133e-09
## 124                         Anaerocolumna_G Kruskal-Wallis 3.200634e-07
## 125                           Anaerofilum_G Kruskal-Wallis 1.254379e-11
## 126                     Anaerosporobacter_G Kruskal-Wallis 3.218415e-05
## 127                          Anaerostipes_G Kruskal-Wallis 1.316182e-06
## 128                              Bacillus_G Kruskal-Wallis 1.174276e-11
## 129                               CAG-352_G Kruskal-Wallis 8.685810e-06
## 130                                CAG-56_G Kruskal-Wallis 1.266937e-11
## 131                              CHKCI001_G Kruskal-Wallis 6.501996e-06
## 132                              CHKCI002_G Kruskal-Wallis 2.413639e-05
## 133                Candidatus Phytoplasma_G Kruskal-Wallis 7.466909e-06
## 134               Candidatus Stoquefichus_G Kruskal-Wallis 4.657966e-17
## 135                     Caproiciproducens_G Kruskal-Wallis 1.982204e-06
## 136                       Catenibacterium_G Kruskal-Wallis 5.744837e-07
## 137                       Christensenella_G Kruskal-Wallis 4.540380e-13
## 138                           Citrobacter_G Kruskal-Wallis 3.269696e-08
## 139                        Cloacibacillus_G Kruskal-Wallis 2.656491e-19
## 140          Clostridium sensu stricto 13_G Kruskal-Wallis 1.765588e-33
## 141           Clostridium sensu stricto 1_G Kruskal-Wallis 1.001200e-29
## 142                           Collinsella_G Kruskal-Wallis 1.584679e-11
## 143                                DTU089_G Kruskal-Wallis 1.469309e-07
## 144                         Desulfovibrio_G Kruskal-Wallis 8.406321e-18
## 145                   Dethiosulfatibacter_G Kruskal-Wallis 3.006610e-48
## 146                           Eggerthella_G Kruskal-Wallis 1.512409e-07
## 147                          Enterobacter_G Kruskal-Wallis 4.283633e-49
## 148                          Epulopiscium_G Kruskal-Wallis 4.252884e-05
## 149                  Escherichia-Shigella_G Kruskal-Wallis 1.211205e-45
## 150                      Faecalibacterium_G Kruskal-Wallis 1.487630e-10
## 151                        Fastidiosipila_G Kruskal-Wallis 1.336125e-06
## 152                         Fermentimonas_G Kruskal-Wallis 2.441946e-07
## 153                            Fusibacter_G Kruskal-Wallis 1.175304e-12
## 154                         Fusobacterium_G Kruskal-Wallis 1.088967e-09
## 155                         GCA-900066575_G Kruskal-Wallis 6.399238e-14
## 156                         GCA-900066755_G Kruskal-Wallis 7.629800e-39
## 157                               Gemella_G Kruskal-Wallis 3.263173e-17
## 158                           Geobacillus_G Kruskal-Wallis 2.557631e-41
## 159                        Granulicatella_G Kruskal-Wallis 2.758976e-05
## 160                          Halobacillus_G Kruskal-Wallis 4.880366e-05
## 161                          Harryflintia_G Kruskal-Wallis 3.870844e-22
## 162                             Hespellia_G Kruskal-Wallis 1.148969e-05
## 163                          Holdemanella_G Kruskal-Wallis 2.340090e-13
## 164                        Intestinimonas_G Kruskal-Wallis 9.422743e-07
## 165                            Klebsiella_G Kruskal-Wallis 1.636362e-24
## 166          Lachnospiraceae NC2004 group_G Kruskal-Wallis 1.126652e-05
## 167           Lachnospiraceae NK4B4 group_G Kruskal-Wallis 3.406312e-05
## 168               Lachnospiraceae UCG-001_G Kruskal-Wallis 3.250466e-05
## 169               Lachnospiraceae UCG-002_G Kruskal-Wallis 2.994427e-09
## 170               Lachnospiraceae UCG-006_G Kruskal-Wallis 3.057389e-05
## 171               Lachnospiraceae UCG-008_G Kruskal-Wallis 3.008204e-05
## 172               Lachnospiraceae UCG-010_G Kruskal-Wallis 1.108805e-05
## 173                    Lacticaseibacillus_G Kruskal-Wallis 9.016788e-11
## 174                        Lysinibacillus_G Kruskal-Wallis 4.843857e-30
## 175                    Macellibacteroides_G Kruskal-Wallis 8.252963e-10
## 176                             Megamonas_G Kruskal-Wallis 2.480778e-05
## 177                              Moryella_G Kruskal-Wallis 1.698210e-05
## 178                      Negativibacillus_G Kruskal-Wallis 3.505090e-12
## 179                            OM27 clade_G Kruskal-Wallis 2.689783e-40
## 180                             Olsenella_G Kruskal-Wallis 6.665175e-09
## 181                          Paludibacter_G Kruskal-Wallis 7.121028e-06
## 182                               Pantoea_G Kruskal-Wallis 1.357796e-40
## 183                       Paraclostridium_G Kruskal-Wallis 2.973228e-10
## 184                        Paraprevotella_G Kruskal-Wallis 3.160620e-10
## 185                            Parvimonas_G Kruskal-Wallis 4.199992e-28
## 186                         Peptoniphilus_G Kruskal-Wallis 1.264624e-13
## 187                    Peptostreptococcus_G Kruskal-Wallis 1.711585e-35
## 188                         Porphyromonas_G Kruskal-Wallis 1.328965e-07
## 189                          Prevotella_7_G Kruskal-Wallis 1.519248e-07
## 190                            Prevotella_G Kruskal-Wallis 5.583780e-06
## 191           Prevotellaceae NK3B31 group_G Kruskal-Wallis 1.352145e-08
## 192                Prevotellaceae UCG-001_G Kruskal-Wallis 3.947135e-08
## 193                         Robinsoniella_G Kruskal-Wallis 7.475667e-08
## 194                            Romboutsia_G Kruskal-Wallis 2.498546e-34
## 195                     Ruminiclostridium_G Kruskal-Wallis 8.539573e-14
## 196                            Salmonella_G Kruskal-Wallis 3.407245e-50
## 197                              Serratia_G Kruskal-Wallis 9.170091e-39
## 198                            Shewanella_G Kruskal-Wallis 6.216394e-14
## 199                           Sporobacter_G Kruskal-Wallis 8.346727e-13
## 200                         Streptococcus_G Kruskal-Wallis 4.895500e-05
## 201                       Subdoligranulum_G Kruskal-Wallis 1.307132e-12
## 202                            Sutterella_G Kruskal-Wallis 3.585245e-19
## 203                          Turicibacter_G Kruskal-Wallis 9.040827e-35
## 204                               UCG-002_G Kruskal-Wallis 1.932637e-09
## 205                               UCG-004_G Kruskal-Wallis 2.840630e-48
## 206                               UCG-005_G Kruskal-Wallis 9.285072e-10
## 207                               UCG-012_G Kruskal-Wallis 3.913332e-05
## 208                           Veillonella_G Kruskal-Wallis 2.021157e-43
## 209                                Vibrio_G Kruskal-Wallis 1.439854e-15
## 210                               XBB1006_G Kruskal-Wallis 4.544741e-06
## 211       [Eubacterium] fissicatena group_G Kruskal-Wallis 6.453837e-08
## 212           [Ruminococcus] gnavus group_G Kruskal-Wallis 2.739855e-11
##     Normal_Distribution Adjusted_P_Value_Bonferroni Adjusted_P_Value_Holm
## 1                    No                3.084681e-04          2.498337e-04
## 2                    No                4.177292e-03          3.294488e-03
## 3                    No                5.045450e-04          4.068527e-04
## 4                    No                4.705606e-07          4.038933e-07
## 5                    No                9.339824e-07          7.972483e-07
## 6                    No                3.490373e-03          2.769222e-03
## 7                    No                1.831277e-19          1.721012e-19
## 8                    No                1.906068e-05          1.591015e-05
## 9                    No                7.764659e-05          6.407906e-05
## 10                   No                2.963605e-13          2.704683e-13
## 11                   No                1.952236e-09          1.730968e-09
## 12                   No                6.576800e-04          5.295605e-04
## 13                   No                9.495882e-29          9.170757e-29
## 14                   No                1.674250e-05          1.399491e-05
## 15                   No                9.261728e-10          8.299471e-10
## 16                   No                1.846499e-09          1.641574e-09
## 17                   No                2.435056e-02          1.854322e-02
## 18                   No                3.161683e-16          2.919051e-16
## 19                   No                2.718142e-25          2.596195e-25
## 20                   No                9.045952e-24          8.586712e-24
## 21                   No                1.237729e-04          1.018533e-04
## 22                   No                1.394866e-02          1.078675e-02
## 23                   No                2.771146e-04          2.250943e-04
## 24                   No                3.894955e-05          3.237365e-05
## 25                   No                2.959108e-40          2.917185e-40
## 26                   No                1.653444e-08          1.442615e-08
## 27                   No                1.952236e-09          1.730968e-09
## 28                   No                1.511060e-30          1.464676e-30
## 29                   No                3.316648e-29          3.207007e-29
## 30                   No                1.637647e-06          1.393432e-06
## 31                   No                4.782713e-04          3.867955e-04
## 32                   No                5.849585e-08          5.069179e-08
## 33                   No                1.103337e-12          1.005639e-12
## 34                   No                1.558741e-23          1.477767e-23
## 35                   No                2.707496e-06          2.295138e-06
## 36                   No                1.073374e-10          9.669235e-11
## 37                   No                2.717241e-09          2.396434e-09
## 38                   No                1.116026e-05          9.381473e-06
## 39                   No                7.716426e-19          7.224470e-19
## 40                   No                7.735170e-03          6.036538e-03
## 41                   No                1.711270e-13          1.563781e-13
## 42                   No                9.929892e-04          7.960327e-04
## 43                   No                2.580438e-04          2.102128e-04
## 44                   No                1.360773e-08          1.188869e-08
## 45                   No                6.110950e-27          5.887291e-27
## 46                   No                1.068919e-40          1.056299e-40
## 47                   No                1.946857e-32          1.898588e-32
## 48                   No                1.661029e-05          1.390401e-05
## 49                   No                3.817410e-03          3.024182e-03
## 50                   No                1.129138e-49          1.129138e-49
## 51                   No                2.430765e-16          2.249965e-16
## 52                   No                1.693644e-02          1.301727e-02
## 53                   No                1.394866e-02          1.078675e-02
## 54                   No                2.177582e-03          1.730239e-03
## 55                   No                3.894955e-05          3.237365e-05
## 56                   No                1.653444e-08          1.442615e-08
## 57                   No                1.952236e-09          1.730968e-09
## 58                   No                4.279384e-21          4.036868e-21
## 59                   No                3.345282e-06          2.827889e-06
## 60                   No                1.217277e-29          1.178474e-29
## 61                   No                1.881618e-02          1.441759e-02
## 62                   No                1.381352e-07          1.190540e-07
## 63                   No                3.508997e-08          3.049140e-08
## 64                   No                2.546547e-04          2.077525e-04
## 65                   No                1.451919e-14          1.333640e-14
## 66                   No                4.702330e-17          4.374777e-17
## 67                   No                4.138427e-03          3.268722e-03
## 68                   No                4.797578e-15          4.418077e-15
## 69                   No                8.516894e-08          7.360527e-08
## 70                   No                1.422975e-26          1.365854e-26
## 71                   No                4.667001e-22          4.419049e-22
## 72                   No                2.089602e-09          1.845363e-09
## 73                   No                1.846499e-09          1.641574e-09
## 74                   No                4.219303e-18          3.935360e-18
## 75                   No                2.149964e-18          2.007817e-18
## 76                   No                8.800762e-03          6.857737e-03
## 77                   No                7.540731e-24          7.166811e-24
## 78                   No                5.566401e-08          4.830348e-08
## 79                   No                3.945788e-09          3.470616e-09
## 80                   No                1.624490e-03          1.294605e-03
## 81                   No                7.783238e-06          6.551888e-06
## 82                   No                2.546599e-45          2.534572e-45
## 83                   No                2.331999e-42          2.307220e-42
## 84                   No                1.207822e-43          1.197840e-43
## 85                   No                1.467286e-04          1.200507e-04
## 86                   No                9.954821e-10          8.908801e-10
## 87                   No                9.577005e-04          7.688741e-04
## 88                   No                2.763908e-14          2.535486e-14
## 89                   No                2.673428e-17          2.490360e-17
## 90                   No                1.137703e-03          9.080131e-04
## 91                   No                2.709161e-25          2.590815e-25
## 92                   No                2.668053e-24          2.538902e-24
## 93                   No                1.862088e-12          1.690609e-12
## 94                   No                4.067318e-35          3.980882e-35
## 95                   No                1.162111e-09          1.035884e-09
## 96                   No                8.748511e-22          8.273385e-22
## 97                   No                1.103337e-12          1.005639e-12
## 98                   No                6.261366e-32          6.098733e-32
## 99                   No                2.707496e-06          2.295138e-06
## 100                  No                3.126587e-15          2.882957e-15
## 101                  No                5.617055e-06          4.741670e-06
## 102                  No                3.385365e-19          3.173530e-19
## 103                  No                7.716426e-19          7.224470e-19
## 104                  No                6.267575e-17          5.823591e-17
## 105                  No                1.340419e-05          1.123610e-05
## 106                  No                2.793711e-04          2.265974e-04
## 107                  No                4.787535e-05          3.967945e-05
## 108                  No                9.275996e-03          7.217097e-03
## 109                  No                9.942614e-21          9.367421e-21
## 110                  No                1.715515e-09          1.527153e-09
## 111                  No                9.596507e-17          8.905378e-17
## 112                  No                3.374340e-44          3.350437e-44
## 113                  No                6.486915e-12          5.881878e-12
## 114                  No                1.581359e-02          1.217292e-02
## 115                  No                8.777168e-35          8.580277e-35
## 116                  No                1.557397e-24          1.483849e-24
## 117                  No                6.333424e-26          6.064235e-26
## 118                  No                1.694780e-02          1.301727e-02
## 119                  No                6.393356e-27          6.151812e-27
## 120                  No                7.965815e-20          7.495578e-20
## 121                  No                4.422218e-03          3.482431e-03
## 122                  No                1.422975e-26          1.365854e-26
## 123                  No                1.355312e-06          1.155296e-06
## 124                  No                2.710937e-04          2.205237e-04
## 125                  No                1.062459e-08          9.320033e-09
## 126                  No                2.725998e-02          2.066223e-02
## 127                  No                1.114807e-03          8.923717e-04
## 128                  No                9.946116e-09          8.736612e-09
## 129                  No                7.356881e-03          5.750006e-03
## 130                  No                1.073095e-08          9.400671e-09
## 131                  No                5.507190e-03          4.323827e-03
## 132                  No                2.044352e-02          1.564038e-02
## 133                  No                6.324472e-03          4.950561e-03
## 134                  No                3.945297e-14          3.609923e-14
## 135                  No                1.678927e-03          1.336006e-03
## 136                  No                4.865877e-04          3.929469e-04
## 137                  No                3.845702e-10          3.455229e-10
## 138                  No                2.769433e-05          2.308406e-05
## 139                  No                2.250048e-16          2.085346e-16
## 140                  No                1.495453e-30          1.451313e-30
## 141                  No                8.480166e-27          8.149770e-27
## 142                  No                1.342223e-08          1.174247e-08
## 143                  No                1.244504e-04          1.022639e-04
## 144                  No                7.120154e-15          6.548524e-15
## 145                  No                2.546599e-45          2.534572e-45
## 146                  No                1.281011e-04          1.051124e-04
## 147                  No                3.628237e-46          3.619670e-46
## 148                  No                3.602192e-02          2.713340e-02
## 149                  No                1.025891e-42          1.016201e-42
## 150                  No                1.260022e-07          1.087457e-07
## 151                  No                1.131698e-03          9.045564e-04
## 152                  No                2.068329e-04          1.689827e-04
## 153                  No                9.954821e-10          8.908801e-10
## 154                  No                9.223555e-07          7.884125e-07
## 155                  No                5.420155e-11          4.901816e-11
## 156                  No                6.462441e-36          6.340364e-36
## 157                  No                2.763908e-14          2.535486e-14
## 158                  No                2.166313e-38          2.133064e-38
## 159                  No                2.336852e-02          1.782298e-02
## 160                  No                4.133670e-02          3.108793e-02
## 161                  No                3.278605e-19          3.077321e-19
## 162                  No                9.731768e-03          7.537237e-03
## 163                  No                1.982056e-10          1.783148e-10
## 164                  No                7.981063e-04          6.416888e-04
## 165                  No                1.385999e-21          1.309090e-21
## 166                  No                9.542746e-03          7.402106e-03
## 167                  No                2.885146e-02          2.180039e-02
## 168                  No                2.753145e-02          2.083549e-02
## 169                  No                2.536280e-06          2.152993e-06
## 170                  No                2.589609e-02          1.965901e-02
## 171                  No                2.547949e-02          1.937284e-02
## 172                  No                9.391580e-03          7.295938e-03
## 173                  No                7.637220e-08          6.609306e-08
## 174                  No                4.102747e-27          3.957431e-27
## 175                  No                6.990259e-07          5.991651e-07
## 176                  No                2.101219e-02          1.605064e-02
## 177                  No                1.438384e-02          1.108931e-02
## 178                  No                2.968811e-09          2.614797e-09
## 179                  No                2.278247e-37          2.237900e-37
## 180                  No                5.645403e-06          4.758935e-06
## 181                  No                6.031510e-03          4.728362e-03
## 182                  No                1.150053e-37          1.131044e-37
## 183                  No                2.518324e-07          2.167483e-07
## 184                  No                2.677045e-07          2.300931e-07
## 185                  No                3.557393e-25          3.393594e-25
## 186                  No                1.071137e-10          9.661731e-11
## 187                  No                1.449712e-32          1.415481e-32
## 188                  No                1.125633e-04          9.276176e-05
## 189                  No                1.286803e-04          1.054358e-04
## 190                  No                4.729462e-03          3.718798e-03
## 191                  No                1.145267e-05          9.613751e-06
## 192                  No                3.343223e-05          2.782730e-05
## 193                  No                6.331890e-05          5.232967e-05
## 194                  No                2.116268e-31          2.056303e-31
## 195                  No                7.233018e-11          6.532773e-11
## 196                  No                2.885937e-47          2.882530e-47
## 197                  No                7.767067e-36          7.611175e-36
## 198                  No                5.265285e-11          4.767974e-11
## 199                  No                7.069677e-10          6.343512e-10
## 200                  No                4.146488e-02          3.113538e-02
## 201                  No                1.107141e-09          9.881918e-10
## 202                  No                3.036703e-16          2.807247e-16
## 203                  No                7.657580e-32          7.449641e-32
## 204                  No                1.636944e-06          1.393432e-06
## 205                  No                2.406013e-45          2.397492e-45
## 206                  No                7.864456e-07          6.731678e-07
## 207                  No                3.314592e-02          2.500619e-02
## 208                  No                1.711920e-40          1.689688e-40
## 209                  No                1.219556e-12          1.108687e-12
## 210                  No                3.849396e-03          3.044977e-03
## 211                  No                5.466400e-05          4.524140e-05
## 212                  No                2.320657e-08          2.019273e-08
##     Adjusted_P_Value_BH test_pass
## 1          1.904124e-06         Y
## 2          2.320718e-05         Y
## 3          3.057848e-06         Y
## 4          3.888931e-09         Y
## 5          7.471859e-09         Y
## 6          1.983167e-05         Y
## 7          3.521687e-21         Y
## 8          1.351821e-07         Y
## 9          5.211180e-07         Y
## 10         3.951474e-15         Y
## 11         1.971956e-11         Y
## 12         3.961927e-06         Y
## 13         3.165294e-30         Y
## 14         1.195893e-07         Y
## 15         1.040644e-11         Y
## 16         1.923436e-11         Y
## 17         1.199535e-04         Y
## 18         4.790429e-18         Y
## 19         6.969596e-27         Y
## 20         2.055898e-25         Y
## 21         8.187529e-07         Y
## 22         7.190032e-05         Y
## 23         1.731966e-06         Y
## 24         2.686176e-07         Y
## 25         2.276237e-41         Y
## 26         1.503131e-10         Y
## 27         1.971956e-11         Y
## 28         5.596519e-32         Y
## 29         1.143672e-30         Y
## 30         1.279412e-08         Y
## 31         2.934179e-06         Y
## 32         5.131214e-10         Y
## 33         1.432905e-14         Y
## 34         3.463868e-25         Y
## 35         2.066791e-08         Y
## 36         1.262793e-12         Y
## 37         2.690338e-11         Y
## 38         8.206076e-08         Y
## 39         1.377933e-20         Y
## 40         4.136455e-05         Y
## 41         2.312527e-15         Y
## 42         5.875676e-06         Y
## 43         1.633189e-06         Y
## 44         1.259975e-10         Y
## 45         1.909672e-28         Y
## 46         9.717447e-42         Y
## 47         8.849349e-34         Y
## 48         1.194985e-07         Y
## 49         2.156729e-05         Y
## 50         1.129138e-49         Y
## 51         3.798071e-18         Y
## 52         8.559495e-05         Y
## 53         7.190032e-05         Y
## 54         1.244332e-05         Y
## 55         2.686176e-07         Y
## 56         1.503131e-10         Y
## 57         1.971956e-11         Y
## 58         8.733436e-23         Y
## 59         2.534305e-08         Y
## 60         4.347418e-31         Y
## 61         9.455367e-05         Y
## 62         1.170637e-09         Y
## 63         3.133033e-10         Y
## 64         1.622004e-06         Y
## 65         2.074170e-16         Y
## 66         7.837216e-19         Y
## 67         2.311971e-05         Y
## 68         7.055262e-17         Y
## 69         7.342150e-10         Y
## 70         3.952707e-28         Y
## 71         1.014565e-23         Y
## 72         2.089602e-11         Y
## 73         1.923436e-11         Y
## 74         7.274661e-20         Y
## 75         3.771866e-20         Y
## 76         4.681256e-05         Y
## 77         1.753658e-25         Y
## 78         4.926019e-10         Y
## 79         3.830862e-11         Y
## 80         9.390114e-06         Y
## 81         5.765362e-08         Y
## 82         4.244331e-46         Y
## 83         2.331999e-43         Y
## 84         1.509777e-44         Y
## 85         9.466363e-07         Y
## 86         1.093936e-11         Y
## 87         5.700598e-06         Y
## 88         3.838761e-16         Y
## 89         4.531234e-19         Y
## 90         6.614551e-06         Y
## 91         6.969596e-27         Y
## 92         6.352506e-26         Y
## 93         2.357074e-14         Y
## 94         2.140694e-36         Y
## 95         1.249581e-11         Y
## 96         1.861385e-23         Y
## 97         1.432905e-14         Y
## 98         2.722333e-33         Y
## 99         2.066791e-08         Y
## 100        4.666547e-17         Y
## 101        4.212988e-08         Y
## 102        6.269195e-21         Y
## 103        1.377933e-20         Y
## 104        1.027471e-18         Y
## 105        9.713180e-08         Y
## 106        1.735224e-06         Y
## 107        3.279133e-07         Y
## 108        4.907935e-05         Y
## 109        1.988523e-22         Y
## 110        1.825016e-11         Y
## 111        1.547824e-18         Y
## 112        4.820486e-45         Y
## 113        8.108644e-14         Y
## 114        8.068160e-05         Y
## 115        4.388584e-36         Y
## 116        3.798530e-26         Y
## 117        1.711736e-27         Y
## 118        8.559495e-05         Y
## 119        1.937381e-28         Y
## 120        1.561925e-21         Y
## 121        2.443214e-05         Y
## 122        3.952707e-28         Y
## 123        1.075645e-08         Y
## 124        1.704992e-06         Y
## 125        1.011865e-10         Y
## 126        1.323300e-04         Y
## 127        6.557686e-06         Y
## 128        9.563573e-11         Y
## 129        3.955312e-05         Y
## 130        1.012354e-10         Y
## 131        3.009394e-05         Y
## 132        1.022176e-04         Y
## 133        3.418633e-05         Y
## 134        5.404516e-16         Y
## 135        9.649006e-06         Y
## 136        2.966998e-06         Y
## 137        4.420347e-12         Y
## 138        1.950305e-07         Y
## 139        3.571505e-18         Y
## 140        5.596519e-32         Y
## 141        2.494167e-28         Y
## 142        1.254414e-10         Y
## 143        8.187529e-07         Y
## 144        1.031906e-16         Y
## 145        4.244331e-46         Y
## 146        8.355864e-07         Y
## 147        1.209412e-46         Y
## 148        1.715330e-04         Y
## 149        1.139878e-43         Y
## 150        1.076942e-09         Y
## 151        6.614551e-06         Y
## 152        1.325852e-06         Y
## 153        1.093936e-11         Y
## 154        7.438351e-09         Y
## 155        6.609945e-13         Y
## 156        3.801436e-37         Y
## 157        3.838761e-16         Y
## 158        1.547366e-39         Y
## 159        1.156858e-04         Y
## 160        1.955891e-04         Y
## 161        6.186047e-21         Y
## 162        5.068629e-05         Y
## 163        2.304716e-12         Y
## 164        4.779080e-06         Y
## 165        2.887497e-23         Y
## 166        4.996202e-05         Y
## 167        1.387089e-04         Y
## 168        1.330022e-04         Y
## 169        1.966108e-08         Y
## 170        1.263224e-04         Y
## 171        1.248995e-04         Y
## 172        4.942937e-05         Y
## 173        6.641061e-10         Y
## 174        1.323467e-28         Y
## 175        5.729721e-09         Y
## 176        1.045383e-04         Y
## 177        7.376328e-05         Y
## 178        2.910599e-11         Y
## 179        1.423904e-38         Y
## 180        4.212988e-08         Y
## 181        3.277995e-05         Y
## 182        7.667023e-39         Y
## 183        2.116239e-09         Y
## 184        2.230871e-09         Y
## 185        8.893484e-27         Y
## 186        1.262793e-12         Y
## 187        6.903392e-34         Y
## 188        7.504222e-07         Y
## 189        8.355864e-07         Y
## 190        2.598605e-05         Y
## 191        8.359612e-08         Y
## 192        2.337918e-07         Y
## 193        4.278304e-07         Y
## 194        8.465073e-33         Y
## 195        8.714480e-13         Y
## 196        1.442968e-47         Y
## 197        4.315037e-37         Y
## 198        6.500352e-13         Y
## 199        8.033724e-12         Y
## 200        1.955891e-04         Y
## 201        1.203414e-11         Y
## 202        4.671850e-18         Y
## 203        3.190658e-33         Y
## 204        1.279412e-08         Y
## 205        4.244331e-46         Y
## 206        6.393867e-09         Y
## 207        1.585929e-04         Y
## 208        1.426600e-41         Y
## 209        1.563533e-14         Y
## 210        2.162582e-05         Y
## 211        3.718639e-07         Y
## 212        2.090682e-10         Y

• Посмотрим на данные здоровых людей и людей с СРК как на многомерный вектор по всем таксонам и группам

combined_bacteria_G <- combined_bacteria_clean %>%
  select(Health_state, ends_with("_G"))

d <- dist(combined_bacteria_G) # euclidean distances between the rows
fit <- cmdscale(d,eig=TRUE, k=2) # k is the number of dim

df_mds <- data.frame(
  x = fit$points[,1],
  y = fit$points[,2]
  )  

df_full <- cbind(df_mds, combined_info) %>% mutate(Health_state_n = case_when(Health_state == "Health"  ~ 0,
                                                                              Health_state == "Disease" ~ 1))

ggplot(df_full, aes(x = x, y = y, color = Health_state)) +
  geom_point() +
  theme_bw() +
  ggtitle("Распределение вектора таксонов в зависимости от группы пациентов")

• Используя метод пермутаций проверим отличаются ли группы в зависимомти от Health_state

adonis2(d ~ Health_state_n, data = df_full)

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs     R2      F Pr(>F)   
## Health_state_n   1     1046 0.0122 4.6817  0.007 **
## Residual       379    84659 0.9878                 
## Total          380    85705 1.0000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Тест Манна-Уитни для сравнения каждого таксона между больными и здоровыми

• после округления всех значений до целого

Wilcox_comparison_round_0 <- data_wide %>% 
  select(Health_state, Archaea, Bacteria,
         ends_with(c("_D", "_P", "_O", "_C", "_F", "_G"))) %>% 
  mutate(across (where (is.numeric), function (x) round (x,0))) %>% 
  summarise_if (is.numeric, function (x) (wilcox.test(x ~ .$Health_state)$p.value)) %>% 
  pivot_longer(everything()) %>% 
  rename (Taxon = name, p_value = value) %>% 
  filter (p_value <= 0.05 ) %>% 
  arrange(p_value) %>% 
  add_column(p_value_holm = p.adjust(.$p_value, "holm")) %>% 
  add_column(p_value_BH = p.adjust(.$p_value, "BH"))

 rbind (
   "Количество значимо различающихся таксонов по p_value" = nrow (Wilcox_comparison_round_0),
 "Количество значимо различающихся таксонов по p_value_holm" = nrow (Wilcox_comparison_round_0 %>% filter (p_value_holm <= 0.05 )),
 "Количество значимо различающихся таксонов по p_value_BH" = nrow (Wilcox_comparison_round_0 %>% filter (p_value_BH <= 0.05 ))
 )

##                                                           [,1]
## Количество значимо различающихся таксонов по p_value       100
## Количество значимо различающихся таксонов по p_value_holm   54
## Количество значимо различающихся таксонов по p_value_BH    100

• после округления всех значений до десятых

Wilcox_comparison_round_1 <- data_wide %>% 
  select(Health_state, Archaea, Bacteria,
         ends_with(c("_D", "_P", "_O", "_C", "_F", "_G"))) %>% 
  mutate(across (where (is.numeric), function (x) round (x,1))) %>% 
  summarise_if (is.numeric, function (x) (wilcox.test(x ~ .$Health_state)$p.value)) %>% 
  pivot_longer(everything()) %>% 
  rename (Taxon = name, p_value = value) %>% 
  filter (p_value <= 0.05 ) %>% 
  arrange(p_value) %>% 
  add_column(p_value_holm = p.adjust(.$p_value, "holm")) %>% 
  add_column(p_value_BH = p.adjust(.$p_value, "BH"))

 rbind (
   "Количество значимо различающихся таксонов по p_value" = nrow (Wilcox_comparison_round_1),
 "Количество значимо различающихся таксонов по p_value_holm" = nrow (Wilcox_comparison_round_1 %>% filter (p_value_holm <= 0.05 )),
 "Количество значимо различающихся таксонов по p_value_BH" = nrow (Wilcox_comparison_round_1 %>% filter (p_value_BH <= 0.05 ))
 )

##                                                           [,1]
## Количество значимо различающихся таксонов по p_value       273
## Количество значимо различающихся таксонов по p_value_holm  155
## Количество значимо различающихся таксонов по p_value_BH    273

• после округления всех значений до сотых

Wilcox_comparison_round_2 <- data_wide %>% 
  select(Health_state, Archaea, Bacteria,
         ends_with(c("_D", "_P", "_O", "_C", "_F", "_G"))) %>% 
  mutate(across (where (is.numeric), function (x) round (x,2))) %>% 
  summarise_if (is.numeric, function (x) (wilcox.test(x ~ .$Health_state)$p.value)) %>% 
  pivot_longer(everything()) %>% 
  rename (Taxon = name, p_value = value) %>% 
  filter (p_value <= 0.05 ) %>% 
  arrange(p_value) %>% 
  add_column(p_value_holm = p.adjust(.$p_value, "holm")) %>% 
  add_column(p_value_BH = p.adjust(.$p_value, "BH"))

 rbind (
   "Количество значимо различающихся таксонов по p_value" = nrow (Wilcox_comparison_round_2),
 "Количество значимо различающихся таксонов по p_value_holm" = nrow (Wilcox_comparison_round_2 %>% filter (p_value_holm <= 0.05 )),
 "Количество значимо различающихся таксонов по p_value_BH" = nrow (Wilcox_comparison_round_2 %>% filter (p_value_BH <= 0.05 ))
 )

##                                                           [,1]
## Количество значимо различающихся таксонов по p_value       599
## Количество значимо различающихся таксонов по p_value_holm  374
## Количество значимо различающихся таксонов по p_value_BH    599

GLM метод для нулей

library("haven")
library("ResourceSelection")  ## Package to perform the Hosmer-Lemeshow GOF test
library("survey")
library("prediction")
library("margins")
library("ggeffects")
library("sjPlot")
library("statmod")
#devtools::install_github("strengejacke/strengejacke")
#install.packages(c("haven", "ResourceSelection", "survey", "prediction", "margins", "ggeffects", "sjPlot", "statmod"))

options(survey.lonely.psu = 'adjust')
start_values <- c(0, 0, 1)

G - Genus (Род)

analyze_taxon <- function(taxon_name, data) {
  tryCatch(
    {
      data_filtered <- data %>%
        filter(Taxon == taxon_name) %>%
        select(patient_ID, Health_state, Percentage, Age_range, research_ID) %>%
        mutate(Health_state_num = ifelse(Health_state == "Health", 0, 1)) # для упрацения интерпритации процентов
      
      mepsdsgn = svydesign(
        id = ~patient_ID,
        strata = ~research_ID,
        weights = NULL,
        data = data_filtered,
        nest = TRUE)
      
      start_values <- c(0, 0, 1)
      
      model <- svyglm(Percentage ~ Health_state_num + research_ID,
                      mepsdsgn,
                      family = tweedie(var.power = 2, link.power = 1),
                      start = start_values)
      
      summary_table <- summary(model)
      
      tidy_output <- tidy(model)
      
      result_table1 <- tidy_output %>%
        filter(term == "Health_state_num") %>%
        select(estimate, p.value)
      
      result_table2 <- as.data.frame(confint(model)["Health_state_num", ])
      
      result_table2_transposed <- t(result_table2)
      
      final_result_table_transposed <- bind_cols(result_table1, result_table2_transposed) %>%
        select(estimate, `2.5 %`, `97.5 %`, p.value)
      
      final_result_table_transposed$Taxon <- taxon_name
      
      return(final_result_table_transposed)
    },
    error = function(e) {
      # Обработка ошибки (можно добавить сообщение или просто вернуть NULL)
      # cat("Error in analyze_taxon for Taxon:", taxon_name, "\n")
      return(NULL)
    }
  )
}

# Применение функции ко всем таксонам
unique_taxa <- unique(G_long$Taxon)

result_list <- lapply(unique_taxa, function(taxon) {
  analyze_result <- analyze_taxon(taxon, G_long)
  if (!is.null(analyze_result)) {
    return(analyze_result)
  } else {
    return(data.frame())  # Вернуть пустой data.frame, чтобы не влиять на bind_rows
  }
})

# Объединение результатов в один dataframe
final_result_df <- bind_rows(result_list) %>%
  mutate(estimate = round(estimate, 3),
         `2.5 %` = round(`2.5 %`, 4),
         `97.5 %` = round(`97.5 %`, 4)
         )

# Коррекция на множественные сравнения по методу Холма
final_result_df$p.adjusted <- round(p.adjust(final_result_df$p.value, method = "holm"),3)

# Фильтрация только статистически значимых результатов
significant_results_G <- final_result_df %>%
  filter(p.adjusted < 0.05) %>%  arrange(desc(abs(estimate)))

 #install.packages("knitr")
 #install.packages("kableExtra")

# Загрузка библиотек
library(knitr)
library(kableExtra)

# Ваш код

# Вывод таблички
cat("Статистически значимые результаты:\n")

## Статистически значимые результаты:

kable(significant_results_G, "html") %>%
  kable_styling(full_width = F)

estimate	2.5 %	97.5 %	p.value	Taxon	p.adjusted
0.114	0.0529	0.1743	0.0002665	Methanobrevibacter_G	0.031
-0.107	-0.1274	-0.0869	0.0000000	Flavobacterium_G	0.000
0.086	0.0564	0.1152	0.0000000	Senegalimassilia_G	0.000
0.065	0.0375	0.0929	0.0000052	Solobacterium_G	0.001
0.058	0.0444	0.0707	0.0000000	Acidaminobacter_G	0.000
-0.050	-0.0664	-0.0329	0.0000000	Alkaliflexus_G	0.000
0.050	0.0277	0.0724	0.0000141	Faecalitalea_G	0.002
0.018	0.0117	0.0242	0.0000000	Lactiplantibacillus_G	0.000
-0.016	-0.0198	-0.0116	0.0000000	Thermacetogenium_G	0.000
0.013	0.0071	0.0197	0.0000338	Flavonifractor_G	0.004
0.013	0.0082	0.0178	0.0000002	Oxalobacter_G	0.000
0.013	0.0095	0.0173	0.0000000	Porphyrobacter_G	0.000
0.010	0.0062	0.0129	0.0000000	Anoxybacillus_G	0.000
0.009	0.0068	0.0113	0.0000000	Anaerostignum_G	0.000
-0.009	-0.0113	-0.0075	0.0000000	Erysipelotrichaceae UCG-002_G	0.000
0.009	0.0071	0.0104	0.0000000	Halochromatium_G	0.000
0.009	0.0063	0.0127	0.0000000	Lachnospiraceae NK4B4 group_G	0.000
-0.007	-0.0101	-0.0038	0.0000201	Candidatus Armantifilum_G	0.003
0.007	0.0057	0.0084	0.0000000	Colwellia_G	0.000
0.007	0.0036	0.0112	0.0001343	Marinobacter_G	0.016
0.006	0.0047	0.0079	0.0000000	Hespellia_G	0.000
-0.006	-0.0086	-0.0030	0.0000666	Rikenella_G	0.008
-0.006	-0.0077	-0.0052	0.0000000	Sedimentibacter_G	0.000
0.004	0.0024	0.0058	0.0000022	Acholeplasma_G	0.000
-0.004	-0.0046	-0.0029	0.0000000	Crocinitomix_G	0.000
0.004	0.0022	0.0052	0.0000021	Lentilactobacillus_G	0.000
-0.004	-0.0062	-0.0024	0.0000098	Macellibacteroides_G	0.001
0.004	0.0030	0.0047	0.0000000	Pseudarthrobacter_G	0.000
0.004	0.0019	0.0054	0.0000631	Rubrobacter_G	0.008
0.004	0.0032	0.0048	0.0000000	Syntrophomonas_G	0.000
-0.003	-0.0036	-0.0016	0.0000004	Achromobacter_G	0.000
0.003	0.0013	0.0041	0.0001600	Antarcticibacterium_G	0.019
-0.003	-0.0047	-0.0019	0.0000036	Coriobacteriaceae UCG-002_G	0.001
-0.003	-0.0037	-0.0016	0.0000009	Desulfurispora_G	0.000
-0.003	-0.0031	-0.0021	0.0000000	Formosa_G	0.000
0.003	0.0015	0.0040	0.0000102	Halobacillus_G	0.001
0.003	0.0022	0.0046	0.0000000	Hassallia_G	0.000
-0.003	-0.0043	-0.0014	0.0001027	Hydrogenispora_G	0.013
-0.003	-0.0035	-0.0023	0.0000000	Lachnotalea_G	0.000
-0.003	-0.0036	-0.0016	0.0000005	Leptococcus JA-3-3Ab_G	0.000
-0.003	-0.0045	-0.0016	0.0000334	Marinifilum_G	0.004
-0.003	-0.0037	-0.0021	0.0000000	Oxobacter_G	0.000
-0.003	-0.0039	-0.0022	0.0000000	Paramaledivibacter_G	0.000
-0.003	-0.0042	-0.0016	0.0000109	Pygmaiobacter_G	0.001
0.003	0.0012	0.0040	0.0001789	RB41_G	0.021
-0.003	-0.0046	-0.0017	0.0000214	Salinibacter_G	0.003
-0.003	-0.0033	-0.0021	0.0000000	Serpentinicella_G	0.000
-0.003	-0.0032	-0.0023	0.0000000	Subsaximicrobium_G	0.000
-0.003	-0.0040	-0.0013	0.0000805	XBB1006_G	0.010
-0.002	-0.0031	-0.0015	0.0000001	Acetoanaerobium_G	0.000
0.002	0.0011	0.0023	0.0000001	Anaerospora_G	0.000
-0.002	-0.0026	-0.0010	0.0000107	Chthoniobacter_G	0.001
0.002	0.0010	0.0028	0.0000743	Nocardia_G	0.009
0.002	0.0012	0.0038	0.0001601	Spirochaeta 2_G	0.019
0.002	0.0012	0.0023	0.0000000	Truepera_G	0.000
-0.001	-0.0020	-0.0008	0.0000040	Aeriscardovia_G	0.001
-0.001	-0.0013	-0.0006	0.0000014	Arcicella_G	0.000
-0.001	-0.0020	-0.0009	0.0000008	Blvii28 wastewater-sludge group_G	0.000
-0.001	-0.0014	-0.0006	0.0000020	Clostridiisalibacter_G	0.000
-0.001	-0.0021	-0.0008	0.0000194	Izimaplasma_G	0.003
-0.001	-0.0021	-0.0007	0.0000428	Lawsonella_G	0.005
0.001	0.0008	0.0017	0.0000000	Leucobacter_G	0.000
-0.001	-0.0014	-0.0006	0.0000029	Pseudoclostridium_G	0.000
0.001	0.0007	0.0017	0.0000031	Sporanaerobacter_G	0.000
-0.001	-0.0018	-0.0007	0.0000117	Virgibacillus_G	0.002
-0.001	-0.0015	-0.0007	0.0000001	[Eubacterium] yurii group_G	0.000

F - Family (семейство)

# Применение функции ко всем таксонам
unique_taxa <- unique(F_long$Taxon)

result_list <- lapply(unique_taxa, function(taxon) {
  analyze_result <- analyze_taxon(taxon, F_long)
  if (!is.null(analyze_result)) {
    return(analyze_result)
  } else {
    return(data.frame())  # Вернуть пустой data.frame, чтобы не влиять на bind_rows
  }
})

# Объединение результатов в один dataframe
final_result_df <- bind_rows(result_list) %>%
  mutate(estimate = round(estimate, 3),
         `2.5 %` = round(`2.5 %`, 4),
         `97.5 %` = round(`97.5 %`, 4)
         )

# Коррекция на множественные сравнения по методу Холма
final_result_df$p.adjusted <- round(p.adjust(final_result_df$p.value, method = "holm"),3)

# Фильтрация только статистически значимых результатов
significant_results_F <- final_result_df %>%
  filter(p.adjusted < 0.05) %>%  arrange(desc(abs(estimate)))

# Вывод таблички
cat("Статистически значимые результаты:\n")

## Статистически значимые результаты:

kable(significant_results_F, "html") %>%
  kable_styling(full_width = F)

estimate	2.5 %	97.5 %	p.value	Taxon	p.adjusted
0.891	0.7287	1.0532	0.0000000	Pedosphaeraceae_F	0.000
0.301	0.2544	0.3471	0.0000000	A4b_F	0.000
-0.080	-0.1152	-0.0445	0.0000117	Methanobacteriaceae_F	0.001
0.063	0.0480	0.0783	0.0000000	Chromatiaceae_F	0.000
0.058	0.0444	0.0707	0.0000000	Acidaminobacteraceae_F	0.000
-0.055	-0.0739	-0.0360	0.0000000	Clade II_F	0.000
-0.021	-0.0329	-0.0097	0.0003409	Puniceicoccaceae_F	0.017
-0.016	-0.0198	-0.0117	0.0000000	Thermacetogeniaceae_F	0.000
0.010	0.0054	0.0150	0.0000326	Sphingomonadaceae_F	0.002
0.007	0.0036	0.0112	0.0001343	Marinobacteraceae_F	0.007
0.007	0.0036	0.0111	0.0001574	Micromonosporaceae_F	0.008
0.006	0.0038	0.0079	0.0000000	Colwelliaceae_F	0.000
0.006	0.0037	0.0082	0.0000004	Hydrogenedensaceae_F	0.000
0.006	0.0051	0.0073	0.0000000	Iamiaceae_F	0.000
0.006	0.0043	0.0076	0.0000000	NS11-12 marine group_F	0.000
-0.006	-0.0077	-0.0052	0.0000000	Sedimentibacteraceae_F	0.000
0.006	0.0034	0.0078	0.0000009	WD2101 soil group_F	0.000
-0.004	-0.0042	-0.0032	0.0000000	Aureobasidiaceae_F	0.000
-0.004	-0.0057	-0.0016	0.0004772	Didymellaceae_F	0.024
0.004	0.0019	0.0054	0.0000631	Rubrobacteriaceae_F	0.004
0.004	0.0031	0.0055	0.0000000	Syntrophomonadaceae_F	0.000
0.004	0.0022	0.0059	0.0000247	Thermomonosporaceae_F	0.001
-0.003	-0.0043	-0.0017	0.0000112	Chthoniobacteraceae_F	0.001
-0.003	-0.0037	-0.0016	0.0000009	Desulfurisporaceae_F	0.000
-0.003	-0.0036	-0.0016	0.0000005	Leptococcaceae_F	0.000
0.003	0.0020	0.0034	0.0000000	Limnochordaceae_F	0.000
0.003	0.0019	0.0033	0.0000000	Obscuribacteraceae_F	0.000
-0.003	-0.0037	-0.0021	0.0000000	Oxobacteraceae_F	0.000
-0.003	-0.0040	-0.0014	0.0000790	Pirellulaceae_F	0.004
0.003	0.0013	0.0040	0.0001420	Pyrinomonadaceae_F	0.008
-0.003	-0.0044	-0.0013	0.0003096	Salinivirgaceae_F	0.016
0.003	0.0020	0.0038	0.0000000	Xanthobacteraceae_F	0.000
0.002	0.0019	0.0030	0.0000000	09D2Z48_F	0.000
-0.002	-0.0026	-0.0012	0.0000004	Izemoplasmataceae_F	0.000
-0.002	-0.0033	-0.0015	0.0000001	PeH15_F	0.000
0.002	0.0007	0.0025	0.0006969	Rs-E47 termite group_F	0.033
0.002	0.0020	0.0028	0.0000000	Simkaniaceae_F	0.000
-0.002	-0.0038	-0.0010	0.0006464	TRA3-20_F	0.031
-0.002	-0.0033	-0.0015	0.0000001	Trichocomaceae_F	0.000
0.002	0.0012	0.0023	0.0000000	Trueperaceae_F	0.000
0.002	0.0016	0.0030	0.0000000	type III_F	0.000
-0.001	-0.0020	-0.0008	0.0000131	01D2Z36_F	0.001
-0.001	-0.0012	-0.0003	0.0005701	Bacteriovoracaceae_F	0.028
-0.001	-0.0019	-0.0009	0.0000003	Cladosporiaceae_F	0.000
0.001	0.0006	0.0021	0.0006965	Halothiobacillaceae_F	0.033
0.001	0.0009	0.0017	0.0000000	Streptosporangiaceae_F	0.000

C - Class (класс)

# Применение функции ко всем таксонам
unique_taxa <- unique(C_long$Taxon)

result_list <- lapply(unique_taxa, function(taxon) {
  analyze_result <- analyze_taxon(taxon, C_long)
  if (!is.null(analyze_result)) {
    return(analyze_result)
  } else {
    return(data.frame())  # Вернуть пустой data.frame, чтобы не влиять на bind_rows
  }
})

# Объединение результатов в один dataframe
final_result_df <- bind_rows(result_list) %>%
  mutate(estimate = round(estimate, 3),
         `2.5 %` = round(`2.5 %`, 4),
         `97.5 %` = round(`97.5 %`, 4)
         )

# Коррекция на множественные сравнения по методу Холма
final_result_df$p.adjusted <- round(p.adjust(final_result_df$p.value, method = "holm"),3)

# Фильтрация только статистически значимых результатов
significant_results_C <- final_result_df %>%
  filter(p.adjusted < 0.05) %>%  arrange(desc(abs(estimate)))

# Вывод таблички
cat("Статистически значимые результаты:\n")

## Статистически значимые результаты:

kable(significant_results_C, "html") %>%
  kable_styling(full_width = F)

estimate	2.5 %	97.5 %	p.value	Taxon	p.adjusted
-0.080	-0.1152	-0.0445	1.17e-05	Methanobacteria_C	0.000
0.036	0.0275	0.0435	0.00e+00	Thermodesulfovibrionia_C	0.000
0.022	0.0172	0.0265	0.00e+00	Armatimonadia_C	0.000
-0.017	-0.0222	-0.0124	0.00e+00	Dothideomycetes_C	0.000
-0.016	-0.0198	-0.0117	0.00e+00	Thermacetogenia_C	0.000
0.014	0.0105	0.0166	0.00e+00	Acidimicrobiia_C	0.000
0.012	0.0090	0.0144	0.00e+00	Chlamydiae_C	0.000
0.010	0.0072	0.0126	0.00e+00	Dehalococcoidia_C	0.000
0.009	0.0064	0.0108	0.00e+00	Desulfotomaculia_C	0.000
-0.006	-0.0074	-0.0036	0.00e+00	D8A-2_C	0.000
0.006	0.0037	0.0082	4.00e-07	Hydrogenedentia_C	0.000
0.006	0.0042	0.0076	0.00e+00	Thermoleophilia_C	0.000
0.006	0.0034	0.0078	1.00e-06	Vicinamibacteria_C	0.000
0.005	0.0030	0.0071	2.80e-06	Ignavibacteria_C	0.000
-0.004	-0.0052	-0.0026	0.00e+00	Eurotiomycetes_C	0.000
0.004	0.0019	0.0054	6.31e-05	Rubrobacteria_C	0.001
0.004	0.0031	0.0055	0.00e+00	Syntrophomonadia_C	0.000
0.004	0.0026	0.0055	0.00e+00	vadinHA49_C	0.000
0.003	0.0017	0.0042	4.70e-06	MB-A2-108_C	0.000

O - Order (порядок)

# Применение функции ко всем таксонам
unique_taxa <- unique(O_long$Taxon)

result_list <- lapply(unique_taxa, function(taxon) {
  analyze_result <- analyze_taxon(taxon, O_long)
  if (!is.null(analyze_result)) {
    return(analyze_result)
  } else {
    return(data.frame())  # Вернуть пустой data.frame, чтобы не влиять на bind_rows
  }
})

# Объединение результатов в один dataframe
final_result_df <- bind_rows(result_list) %>%
  mutate(estimate = round(estimate, 3),
         `2.5 %` = round(`2.5 %`, 4),
         `97.5 %` = round(`97.5 %`, 4)
         )

# Коррекция на множественные сравнения по методу Холма
final_result_df$p.adjusted <- round(p.adjust(final_result_df$p.value, method = "holm"),3)

# Фильтрация только статистически значимых результатов
significant_results_O <- final_result_df %>%
  filter(p.adjusted < 0.05) %>%  arrange(desc(abs(estimate)))

# Вывод таблички
cat("Статистически значимые результаты:\n")

## Статистически значимые результаты:

kable(significant_results_O, "html") %>%
  kable_styling(full_width = F)

estimate	2.5 %	97.5 %	p.value	Taxon	p.adjusted
0.891	0.7287	1.0532	0.0000000	Pedosphaerales_O	0.000
-0.080	-0.1152	-0.0445	0.0000117	Methanobacteriales_O	0.000
0.062	0.0465	0.0773	0.0000000	Chromatiales_O	0.000
0.024	0.0176	0.0306	0.0000000	Rhizobiales_O	0.000
0.022	0.0172	0.0265	0.0000000	Armatimonadales_O	0.000
-0.016	-0.0198	-0.0117	0.0000000	Thermacetogeniales_O	0.000
-0.012	-0.0141	-0.0100	0.0000000	Candidatus Kerfeldbacteria_O	0.000
-0.011	-0.0162	-0.0051	0.0001987	Hypocreales_O	0.007
0.010	0.0087	0.0120	0.0000000	LD1-PA32_O	0.000
0.010	0.0054	0.0150	0.0000326	Sphingomonadales_O	0.001
0.008	0.0054	0.0096	0.0000000	Desulfotomaculales_O	0.000
0.008	0.0059	0.0106	0.0000000	Microtrichales_O	0.000
-0.007	-0.0093	-0.0046	0.0000000	Chthoniobacterales_O	0.000
0.007	0.0036	0.0111	0.0001574	Micromonosporales_O	0.006
0.006	0.0037	0.0082	0.0000004	Hydrogenedentiales_O	0.000
-0.006	-0.0092	-0.0033	0.0000447	Pleosporales_O	0.002
-0.005	-0.0051	-0.0040	0.0000000	Dothideales_O	0.000
0.005	0.0032	0.0077	0.0000024	Tepidisphaerales_O	0.000
0.004	0.0014	0.0058	0.0015949	Entomoplasmatales_O	0.048
0.004	0.0019	0.0054	0.0000631	Rubrobacterales_O	0.002
0.004	0.0031	0.0055	0.0000000	Syntrophomonadales_O	0.000
-0.003	-0.0036	-0.0016	0.0000005	Eurycoccales_O	0.000
0.003	0.0022	0.0037	0.0000000	Limnochordales_O	0.000
0.003	0.0019	0.0033	0.0000000	Obscuribacterales_O	0.000
-0.003	-0.0040	-0.0014	0.0000790	Pirellulales_O	0.003
0.003	0.0013	0.0040	0.0001420	Pyrinomonadales_O	0.005
-0.003	-0.0042	-0.0017	0.0000072	RBG-13-54-9_O	0.000
0.003	0.0015	0.0053	0.0003859	Synechococcales_O	0.012
0.003	0.0019	0.0034	0.0000000	eub62A3_O	0.000
0.002	0.0009	0.0027	0.0000904	Candidatus Abawacabacteria_O	0.003
0.002	0.0010	0.0021	0.0000002	Candidatus Peregrinibacteria_O	0.000
0.002	0.0011	0.0020	0.0000000	Candidatus Terrybacteria_O	0.000
-0.002	-0.0029	-0.0012	0.0000018	Capnodiales_O	0.000
-0.002	-0.0033	-0.0015	0.0000001	Eurotiales_O	0.000
0.002	0.0017	0.0033	0.0000000	Gaiellales_O	0.000
0.002	0.0009	0.0032	0.0003267	Ignavibacteriales_O	0.011
-0.001	-0.0012	-0.0003	0.0005701	Bacteriovoracales_O	0.018
0.001	0.0008	0.0018	0.0000027	S085_O	0.000

P - Phylum (тип)

# Применение функции ко всем таксонам
unique_taxa <- unique(P_long$Taxon)

result_list <- lapply(unique_taxa, function(taxon) {
  analyze_result <- analyze_taxon(taxon, P_long)
  if (!is.null(analyze_result)) {
    return(analyze_result)
  } else {
    return(data.frame())  # Вернуть пустой data.frame, чтобы не влиять на bind_rows
  }
})

# Объединение результатов в один dataframe
final_result_df <- bind_rows(result_list) %>%
  mutate(estimate = round(estimate, 3),
         `2.5 %` = round(`2.5 %`, 4),
         `97.5 %` = round(`97.5 %`, 4)
         )

# Коррекция на множественные сравнения по методу Холма
final_result_df$p.adjusted <- round(p.adjust(final_result_df$p.value, method = "holm"),3)

# Фильтрация только статистически значимых результатов
significant_results_P <- final_result_df %>%
  filter(p.adjusted < 0.05) %>%  arrange(desc(abs(estimate)))

# Вывод таблички
cat("Статистически значимые результаты:\n")

## Статистически значимые результаты:

kable(significant_results_P, "html") %>%
  kable_styling(full_width = F)

estimate	2.5 %	97.5 %	p.value	Taxon	p.adjusted
-15.536	-22.0641	-9.0082	4.00e-06	Firmicutes_P	0.000
-0.080	-0.1152	-0.0445	1.17e-05	Euryarchaeota_P	0.000
0.038	0.0302	0.0467	0.00e+00	Armatimonadota_P	0.000
0.038	0.0292	0.0458	0.00e+00	Nitrospirota_P	0.000
-0.015	-0.0212	-0.0090	1.50e-06	Basidiomycota_P	0.000
0.006	0.0037	0.0082	4.00e-07	Hydrogenedentes_P	0.000
0.006	0.0032	0.0097	9.66e-05	Myxococcota_P	0.001

Распределения таксонов G(Genus - Род)

Гистограмма по количеству пациентов (Здоров - СРК)

create_and_plot_taxon_groups <- function(filter_pattern, step_size, dataset_name) {
  
  # Получаем уникальные таксоны, удовлетворяющие условиям
  unique_taxa <- Wilcox_comparison_round_2 %>%
    subset(grepl(filter_pattern, Taxon)) %>%
    filter(p_value_holm < 0.05) %>%
    distinct(Taxon)
  
  # Разбиваем уникальные таксоны на группы по step_size
  taxon_groups <- split(unique_taxa, rep(1:ceiling(nrow(unique_taxa) / step_size), each = step_size, length.out = nrow(unique_taxa)))
  
  # Проходим по группам и строим графики
  for (i in seq_along(taxon_groups)) {
    current_taxa <- taxon_groups[[i]]
    
    current_dataset <- get(dataset_name)  # Получаем датасет по его имени
    
    G_long_filtered <- current_dataset %>%
      filter(Taxon %in% current_taxa$Taxon & Percentage > 0) %>%
      group_by(Health_state, Taxon) %>%
      summarise(Count = n(), .groups = "drop")
    
    bar_plot <- ggplot(G_long_filtered, aes(x = Health_state, y = Count, fill = Health_state == "Disease")) +
      geom_bar(position = "dodge", color = "black", stat = "identity") +
      scale_fill_manual(values = c("skyblue", "red"), guide = FALSE) +
      labs(title = paste("Гистограмма для таксонов", (i - 1) * step_size + 1, "до", min(i * step_size, nrow(unique_taxa)), "в разрезе Health_state"),
           x = "Статус пациента",
           y = "Количество пациентов, у которых обнаружен данный таксон") +
      coord_flip() +
      facet_wrap(~Taxon, scales = "free_y", ncol = 2, shrink = 0.7) +  # Уменьшение пространства между фасетами
      theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 5),  # Поворот текста на оси X и уменьшение шрифта
            axis.text.y = element_text(hjust = 1, size = 5),
            strip.text = element_text(size = 5))  # Уменьшение шрифта для названия фасет
    
    print(bar_plot)  # Отображение графика на экране
    
  }
}

# Пример вызова функции с передачей "_G" в качестве аргумента и названия датасета "G_long"
create_and_plot_taxon_groups("_G", 18, "G_long")

график солнышко

create_and_plot_circular_diagrams <- function(dataset, filter_pattern, step_size, filename) {
  
  # Получаем уникальные таксоны, удовлетворяющие условиям
  unique_taxa <- Wilcox_comparison_round_2 %>%
    subset(grepl(filter_pattern, Taxon)) %>%
    filter(p_value_holm < 0.05) %>%
    distinct(Taxon)
  
  # Разбиваем уникальные таксоны на группы по step_size
  taxon_groups <- split(unique_taxa, rep(1:ceiling(nrow(unique_taxa) / step_size), each = step_size, length.out = nrow(unique_taxa)))
  
  # Проходим по группам и строим графики
  for (i in seq_along(taxon_groups)) {
    current_taxa <- taxon_groups[[i]]
    
    G_long_filtered <- dataset %>%
      filter(Taxon %in% current_taxa$Taxon & Percentage > 0) %>%
      group_by(Health_state, Taxon) %>%
      summarise(Count = n(), .groups = "drop")
    
    # Добавляем строки с Count = 0 для всех комбинаций Health_state и Taxon
    G_long_filtered <- G_long_filtered %>%
      complete(Health_state, Taxon, fill = list(Count = 0))
    
    data_id <- G_long_filtered %>% filter(G_long_filtered$Health_state == "Disease") %>%
      mutate(id = row_number()) %>% 
      select(Taxon, id)
    
    G_long_filtered <- left_join(G_long_filtered, data_id, by = "Taxon")
    
    labels_data <- G_long_filtered %>% filter(Health_state == "Disease")
    number_of_bars <- nrow(labels_data)
    
    #Вычислим углы для лейбла каждого барра
    labels_data$angel <- 90 - 360 * (labels_data$id-0.5) / number_of_bars
    
    # Добавим горизонтольную регулировку
    labels_data <- labels_data %>%
      mutate(hjust = ifelse(angel < -90, 1, 0))
    
    # Перевернем лейбл в зависимости от "полушария"
    labels_data <- labels_data %>%
      mutate(angel = ifelse(angel < -90, angel + 180, angel))
    
    # Круговая диаграмма для объединенного датасета
    p <- ggplot(data = G_long_filtered, mapping = aes(x = id, y = Count, fill = Health_state)) + 
      geom_bar(stat = "identity", position = "stack") + 
      ylim(-150, 500) +
      
      # Добавляем кастомную тему
      theme_minimal(base_size = 8) +
      theme(axis.text   = element_blank(),
            axis.title  = element_blank(),
            panel.grid  = element_blank(),
            plot.margin = unit(rep(-1, 6), "cm")) +
      coord_polar(start = 0)+
      geom_text(data = labels_data,
                aes(x = id, y = Count,
                    label = Taxon,
                    hjust = hjust),
                color = "black",
                fontface = "bold",
                alpha = 0.9,
                size = 1.5,
                angle = labels_data$angel,
                inherit.aes = FALSE) +
      # Добавляем горизонтальные линии сетки
      geom_hline(yintercept = seq(0, 300, by = 50),
                 linetype = "dotted",
                 color = "gray",
                 size = 0.5) +
      # Добавляем значения рядом с линиями сетки
      annotate("text", x = 1.2, y = seq(0, 300, by = 50),
               label = seq(0, 300, by = 50),
               color = "black",
               size = 3,
               alpha = 40,
               hjust = 0)
    
    p <- p + annotate("text", x = 0.5, y = 490, label = "Сотношение здоровых пациентов и пациентов с СРК",
                      size = 5, color = "black", fontface = "bold", hjust = 0.5)
    
    # Сформируем имя файла для сохранения
    current_filename <- paste0(filename, "_", i, ".png")
    
    # Сохраняем график под уникальным именем
    ggsave(current_filename, p, width = 10, height = 8)
    
    print(p)
  }
}

# Пример вызова функции
create_and_plot_circular_diagrams(G_long, "_G", 40, "output_plot")

Боксплот по возрасту

create_and_plot_boxplots <- function(G_long, filter_pattern, step_size, x_var, title) {
  
  # Получаем уникальные таксоны, удовлетворяющие условиям
  unique_taxa <- Wilcox_comparison_round_2 %>%
    subset(grepl(filter_pattern, Taxon)) %>%
    filter(p_value_holm < 0.05) %>%
    distinct(Taxon)
  
  # Разбиваем уникальные таксоны на группы по step_size
  taxon_groups <- split(unique_taxa, rep(1:ceiling(nrow(unique_taxa) / step_size), each = step_size, length.out = nrow(unique_taxa)))
  
  # Проходим по группам и строим боксплоты
  for (i in seq_along(taxon_groups)) {
    current_taxa <- taxon_groups[[i]]
    
    G_long_filtered <- G_long %>%
      filter(Taxon %in% current_taxa$Taxon & Percentage > 0)
    
    # Создаем формулу для переменной x_var
    formula <- as.formula(paste("Age ~", x_var))
    
    # Строим боксплот
    box_plot <- ggplot(G_long_filtered, aes_string(x = x_var, y = "Age", fill = "Health_state")) +
      geom_boxplot() +
      labs(title = paste(title),
           x = x_var,
           y = "Age") +
      theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),  # Уменьшаем размер шрифта для подписей Taxon
            axis.title.x = element_blank(),  # Убираем название оси X
            legend.position = "bottom") +  # Перемещаем легенду вниз
      scale_fill_manual(values = c("red", "skyblue")) +
      facet_grid(~Taxon, scales = "free_y", space = "free")  # Используем facet_grid вместо facet_wrap
    
    # Отображаем график на экране
    print(box_plot)
  }
}

# Пример вызова функции с Health_state в качестве x_var
create_and_plot_boxplots(G_long, "_G", 4, "Health_state", "Боксплоты для таксонов по статусу: Здоров/СРК")

Боксплот по возросту и полу

create_and_plot_boxplots(G_long, "_G", 3, "interaction(Health_state, Sex)", "Боксплоты для таксона (Пол + статус заболевания)")

Боксплот по возросту и статусу курения

create_and_plot_boxplots(G_long, "_G", 3, "interaction(Health_state, Smoking)", "Боксплоты для таксона (Статус курения + статус заболевания)")

Боксплот по возросту и статусу употребления алкоголя

create_and_plot_boxplots(G_long, "_G", 3, "interaction(Health_state, Alcohol)", "Боксплоты для таксона (Статус употребления алкоголя + статус заболевания)")

Распределения таксонов F Family(семейство)

Гистограмма по количеству пациентов (Здоров - СРК)

create_and_plot_taxon_groups("_F", 18, "F_long")

Круговая столбчатая диаграмма для отобрадения отношения здоровых пациентов и пациентов с СРК

create_and_plot_circular_diagrams(F_long, "_F", 40, "F_output_plot")

Боксплот по возрасту

create_and_plot_boxplots(F_long, "_F", 4, "Health_state", "Боксплоты для таксонов по статусу: Здоров/СРК")

Боксплот по возросту и полу

create_and_plot_boxplots(F_long, "_F", 3, "interaction(Health_state, Sex)", "Боксплоты для таксона (Пол + статус заболевания)")

Боксплот по возросту и статусу курения

create_and_plot_boxplots(F_long, "_F", 3, "interaction(Health_state, Smoking)", "Боксплоты для таксона (Статус курения + статус заболевания)")

Боксплот по возросту и статусу употребления алкоголя

create_and_plot_boxplots(F_long, "_F", 3, "interaction(Health_state, Alcohol)", "Боксплоты для таксона (Статус употребления алкоголя + статус заболевания)")

Распределения таксонов C - Class (класс)

Гистограмма по количеству пациентов (Здоров - СРК)

create_and_plot_taxon_groups("_C", 18, "C_long")

Круговая столбчатая диаграмма для отобрадения отношения здоровых пациентов и пациентов с СРК

create_and_plot_circular_diagrams(C_long, "_C", 40, "F_output_plot")

Боксплот по возрасту

create_and_plot_boxplots(C_long, "_C", 4, "Health_state", "Боксплоты для таксонов по статусу: Здоров/СРК")

Боксплот по возросту и полу

create_and_plot_boxplots(C_long, "_C", 3, "interaction(Health_state, Sex)", "Боксплоты для таксона (Пол + статус заболевания)")

Боксплот по возросту и статусу курения

create_and_plot_boxplots(C_long, "_C", 3, "interaction(Health_state, Smoking)", "Боксплоты для таксона (Статус курения + статус заболевания)")

Боксплот по возросту и статусу употребления алкоголя

create_and_plot_boxplots(C_long, "_C", 3, "interaction(Health_state, Alcohol)", "Боксплоты для таксона (Статус употребления алкоголя + статус заболевания)")

Распределения таксонов O - Order (порядок)

Гистограмма по количеству пациентов (Здоров - СРК)

create_and_plot_taxon_groups("_O", 18, "O_long")

Круговая столбчатая диаграмма для отобрадения отношения здоровых пациентов и пациентов с СРК

create_and_plot_circular_diagrams(O_long, "_O", 30, "F_output_plot")

Боксплот по возрасту

create_and_plot_boxplots(O_long, "_O", 4, "Health_state", "Боксплоты для таксонов по статусу: Здоров/СРК")

Боксплот по возросту и полу

create_and_plot_boxplots(O_long, "_O", 3, "interaction(Health_state, Sex)", "Боксплоты для таксона (Пол + статус заболевания)")

Боксплот по возросту и статусу курения

create_and_plot_boxplots(O_long, "_O", 3, "interaction(Health_state, Smoking)", "Боксплоты для таксона (Статус курения + статус заболевания)")

Боксплот по возросту и статусу употребления алкоголя

create_and_plot_boxplots(O_long, "_O", 3, "interaction(Health_state, Alcohol)", "Боксплоты для таксона (Статус употребления алкоголя + статус заболевания)")

Распределения таксонов P - Phylum (тип)

Гистограмма по количеству пациентов (Здоров - СРК)

create_and_plot_taxon_groups("_P", 18, "P_long")

Круговая столбчатая диаграмма для отобрадения отношения здоровых пациентов и пациентов с СРК

create_and_plot_circular_diagrams(P_long, "_P", 30, "P_output_plot")

Боксплот по возрасту

create_and_plot_boxplots(P_long, "_P", 4, "Health_state", "Боксплоты для таксонов по статусу: Здоров/СРК")

Боксплот по возросту и полу

create_and_plot_boxplots(P_long, "_P", 3, "interaction(Health_state, Sex)", "Боксплоты для таксона (Пол + статус заболевания)")

Боксплот по возросту и статусу курения

create_and_plot_boxplots(P_long, "_P", 3, "interaction(Health_state, Smoking)", "Боксплоты для таксона (Статус курения + статус заболевания)")

Боксплот по возросту и статусу употребления алкоголя

create_and_plot_boxplots(P_long, "_P", 3, "interaction(Health_state, Alcohol)", "Боксплоты для таксона (Статус употребления алкоголя + статус заболевания)")

Посмотрим на данные здоровых людей и людей с СРК как на многомерный вектор по всем таксонам и группам

G - Genus

perform_permutation_test <- function(data, filter_pattern) {
  
  combined_bacteria_G <- data %>%
    select(Health_state, ends_with(filter_pattern))
  
  d <- dist(combined_bacteria_G) # euclidean distances between the rows
  fit <- cmdscale(d, eig=TRUE, k=2) # k is the number of dim
  
  df_mds <- data.frame(
    x = fit$points[,1],
    y = fit$points[,2]
  )  
  
  df_full <- cbind(df_mds, combined_info) %>% 
    mutate(Health_state_n = case_when(Health_state == "Health"  ~ 0,
                                      Health_state == "Disease" ~ 1))
  
  msd_plot <- ggplot(df_full, aes(x = x, y = y, color = Health_state)) +
    geom_point() +
    theme_bw() +
    ggtitle("Распределение вектора таксонов в зависимости от группы пациентов")
  
  print(msd_plot)
  
  print("Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state")
  
  adonis2(d ~ Health_state_n, data = df_full)
}

# Пример вызова функции с использованием другого фильтра
perform_permutation_test(combined_bacteria_clean, "_G")

## [1] "Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state"

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs     R2      F Pr(>F)   
## Health_state_n   1     1046 0.0122 4.6817  0.004 **
## Residual       379    84659 0.9878                 
## Total          380    85705 1.0000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

F - Family (семейство)

perform_permutation_test(combined_bacteria_clean, "_F")

## [1] "Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state"

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs      R2      F Pr(>F)   
## Health_state_n   1     1919 0.01423 5.4706  0.002 **
## Residual       379   132940 0.98577                 
## Total          380   134859 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

C - Class (класс)

perform_permutation_test(combined_bacteria_clean, "_C")

## [1] "Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state"

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs      R2      F Pr(>F)    
## Health_state_n   1    32982 0.12662 54.948  0.001 ***
## Residual       379   227490 0.87338                  
## Total          380   260472 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

O - Order (порядок)

perform_permutation_test(combined_bacteria_clean, "_O")

## [1] "Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state"

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs      R2     F Pr(>F)    
## Health_state_n   1     6923 0.04767 18.97  0.001 ***
## Residual       379   138309 0.95233                 
## Total          380   145232 1.00000                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

P - Phylum (тип)

perform_permutation_test(combined_bacteria_clean, "_P")

## [1] "Используя метод пермутаций проверим отличаются ли группы в зависимости от Health_state"

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = d ~ Health_state_n, data = df_full)
##                 Df SumOfSqs      R2      F Pr(>F)    
## Health_state_n   1    32728 0.12502 54.155  0.001 ***
## Residual       379   229042 0.87498                  
## Total          380   261770 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Влияние серотонинпродуцирующих бактерий

library("lme4")
library("stringr")


#Переменная имеет биномиальное распределение
plot(data_long$Серотонин)

#Процент пропущенных значений в переменной Серотонин 96,67%
na_serotonin <- sum(is.na(data_long$Серотонин)) / nrow(data_long) * 100


#Данные о серотонине есть только в F и G taxons
serotonin_taxons <- data_long %>%
  filter(!is.na(`Серотонин`)) %>%
  mutate(Serotonin_taxons = str_sub(Taxon, -2)) %>%
  distinct(Serotonin_taxons) %>%
  pull(Serotonin_taxons)

#При этом только 2 F таксона и 42 G таксона
serotonin_taxons_count_unique <- bac_functions %>%
  filter(!is.na(Серотонин)) %>%
  mutate(Serotonin_taxons = str_sub(Taxon, -2)) %>%
  count(Serotonin_taxons)

#А всего 219 F в датасете
num_unique_F <- data_long %>%
  filter(str_detect(Taxon, "_F$")) %>%
  summarise(Num_Unique = n_distinct(Taxon)) %>%
  pull(Num_Unique)

###Добавляем данные по родам из двух семейств в bac_functions (данные добавлены в 8-й лист Bacterial group functions.xlsx)

# Чтение листов Excel-файла с функциями бактерий и их объединение
path <- "data/raw/Bacterial group functions.xlsx"
taxon <- c ("TaxonName", "Rank")

neuromediators_1 <- read_xlsx (path, 8) %>% 
  mutate(Destroy = ifelse(is.na (Destroy), "produce", "destroy")) %>% 
  unique() %>%
  pivot_wider(names_from = Neuromediator, values_from = Destroy)

probiotics <- read_xlsx (path, 3) %>% 
  add_column(probiotics = 1)

special_properties <- read_xlsx (path, 4) %>% 
  add_column(special_properties = 1)

vitamins <- read_xlsx (path, 5) %>% 
  pivot_wider (names_from = Vitamin, values_from = Vitamin,
               values_fn = function(x) ifelse(is.na (x), NA, 1))

habbits <- read_xlsx (path, 7) %>%
  unique() %>% #удаление повторяющихся строк 
  pivot_wider(names_from = Habbit, values_from = Habit_state)

bac_functions_1 <- read_xlsx (path, 1) %>% #Патогены и нежелательные
  full_join(neuromediators_1, by = taxon) %>% #Нейромедиаторы
  full_join(probiotics, by = taxon) %>% #Пробиотики
  full_join(special_properties, by = taxon) %>% #С особыми свойствами
  full_join(vitamins, by = taxon) %>%  #Витамины
  full_join(read_xlsx (path, 6), by = taxon) %>% #Продуценты КЦДК
  full_join(habbits, by = taxon) %>% #Вредные привычки
  
  unite("Taxon", TaxonName, Rank, sep = "_") %>% 
  filter (Taxon != "Blautia obeum_S") %>% #для данного таксона противоречивая информация в Продуценты КЦЖК
  mutate_all(as.factor)

rm (path, taxon, neuromediators_1, probiotics, special_properties, vitamins, habbits)


data_long_1 <- data_wide %>% 
  pivot_longer(ends_with(c("_D", "_P", "_O", "_C", "_F", "_G")),
               names_to = "Taxon", values_to = "Percentage")

#Перезапись data_long с добавлением функций бактерий
data_long_1 <- data_long_1 %>% 
  left_join (bac_functions_1, by = "Taxon")


G_long_1 <- data_long_1 %>% subset(grepl("_G", Taxon)) 

#Модель отдельно для таксонов G уровня

G_long_serotonin <- G_long_1 %>%
  filter( ! is.na(`Серотонин`)) %>%
  filter(Percentage > 0.0000)

G_long_serotonin$`Серотонин` <- relevel(G_long_serotonin$`Серотонин`, ref = "destroy")

G_long_serotonin$research_ID <- as.factor(G_long_serotonin$research_ID)
G_long_serotonin$Seq_date <- as.factor(G_long_serotonin$Seq_date)
G_long_serotonin$Seq_date <- as.factor(G_long_serotonin$Seq_date)
G_long_serotonin$patient_ID <- as.factor(G_long_serotonin$patient_ID)



model_ser_G_1 <- glmer(Health_state ~ Серотонин +  (1 | research_ID), data = G_long_serotonin, family = binomial)
summary(model_ser_G_1)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: Health_state ~ Серотонин + (1 | research_ID)
##    Data: G_long_serotonin
## 
##      AIC      BIC   logLik deviance df.resid 
##   8002.6   8027.1  -3998.3   7996.6    26740 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.58164 -0.00200  0.00227  0.00291  0.67902 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  research_ID (Intercept) 482.6    21.97   
## Number of obs: 26743, groups:  research_ID, 7
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)  
## (Intercept)       0.30150    1.93365   0.156   0.8761  
## Серотонинproduce  0.14273    0.07275   1.962   0.0498 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## Серотонинpr 0.000

Модель не показывает значимой ассоциации наличия серотонинпродуцирующих бактерий и состоянием здоровья человека (СРК/здоровый).